Corrosion Behavior of Automotive Al-Mg-Si Alloys and Assessment of the Ability of

Accelerated Corrosion Tests to Predict On-road Corrosion of these Alloys

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

By

Dadi Zhang

Materials Science and Engineering

The Ohio State University

2020

Dissertation Committee

Professor Jenifer S. Locke, Advisor

Professor Gerald S. Frankel

Professor Christopher Taylor

Professor Glenn Daehn

1

Copyrighted by

Dadi Zhang

2020

2 Abstract

The growing demand for weight reduction of automobiles is leading to an increased application of Al alloys, such as 6XXX Al-Mg-Si alloys, as replacements for traditional steels. Although Al alloys usually have good corrosion resistance, they can be susceptible to localized corrosion when subjected to environments with corrosive salts, like wet winter roads. It is necessary to evaluate the corrosion resistance of Al alloys in a way that properly simulates long-term exposure in automotive environments. Accelerated tests are a popular method to evaluate long-term corrosion performance in the automobile industry, but since most of the accelerated tests were designed for steels, corrosion behavior of Al-Mg-Si alloys in accelerated tests has not been well correlated with exposure on actual in-use vehicles.

Three wrought Al-Mg-Si alloys (AA6061, AA6022, and C26N) with either different chemical compositions or tempering conditions were exposed to both laboratory accelerated corrosion tests and on-road exposure. C26N is the designation of a 6XXX Al alloy manufactured by Arconic Inc. that has yet to be given a designation by the

Aluminum Association. The on-road exposure was conducted for up to 2 years utilizing a plastic rack affixed under a bus operating on The Ohio State University’s campus where

NaCl based deicing agents were used during wintertime.

ii These alloys were also exposed to laboratory accelerated corrosion tests in order to identify corrosion behavior in accelerated corrosion tests and correlate environmental factors (i.e. pH, wet/dry cycle, cathodic accelerators) to specific corrosion morphologies.

The laboratory corrosion test methodologies examined include an immersion test, ASTM

G110, and salt spray tests of ASTM B117, ASTM B368 (CASS), ASTM G85-A2

(MASTMAASIS), cyclic B117, and GMW 14872. The cyclic B117 test was a modified version of ASTM B117 designed to introduce wet/dry cycles.

Corrosion morphology (e.g. pitting, intergranular corrosion, and intragranular corrosion) after laboratory tests and field exposure were investigated by SEM and cross- sectional analysis. A combination of pitting/grain fallout and intergranular corrosion was observed on all the tested alloys after on-road exposure and the laboratory tests using

NaCl-based solutions of about pH 3. In order to quantitatively evaluate the similarity between the laboratory tests and field exposure, optical micrographs from the cross- section of each localized attack site were binarized to reveal the boundaries of the corrosion feature. The corrosion morphology after exposure was characterized by the fractal dimension using the box-counting method and the ratio of corrosion feature boundary length to the corroded area. Comparing the results from the accelerated tests with those from on-road exposure, pH is observed to have the greatest effect on corrosion morphology, as the laboratory tests using an acidified solution showed the best correlation with on-road exposure results. The tests that used neutral solutions, ASTM

B117, ASTM G110, and GMW14872 were not able to generate IGC on 6022-T4 and

6061-T4 as was observed on these alloys after on-road exposure.

iii GoogLeNet [1], an open-source convolutional neural network (CNN), was used to assess the viability of using optical micrographs of cross-sections of on-road localized attack to predict the proper laboratory accelerated test for future predictions of performance. Fine-tuning of the pre-trained network was conducted by retraining the final layer with cross-sectional images from the laboratory corrosion tests. The validation accuracy reached around 80% for all the three alloys. The results obtained from implementing the CNN also suggest that the localized attack morphology obtained in the tests with low pH was more similar to that observed in the on-road exposures.

The effects of microstructural features on localized corrosion of Al-Mg-Si alloys in accelerated tests were also investigated. After the secondary phase particles were identified with SEM/EDS, potentiodynamic polarization was conducted using an electrochemical microcell on bulk casts of the secondary phases, e.g. Mg2Si, Al5FeSi,

AlFe(MnCr)Si, and Q-Al4Mg8Si7Cu2. Coarse Fe-rich intermetallic particles (IMPs) were the most common second phase that correlated to localized corrosion in the accelerated tests using solutions without acidification. Distribution of second phase particles (size of cluster) was evaluated using SEM over a large area. It was found that individual cathodic

IMPs are not able to cause a stable pit because the trench around the particle was too shallow to form a stable pit before the removal of the particle. Possibility for the formation of stable pits increases with higher particle density and throwing power due to particle clustering. Susceptibility to IGC of 6061-T4 increases after artificial aging, but the formation Cu-rich Q phase is likely not necessarily related to the increased susceptibility owing to the similar OCP of Q phase to high-purity Al.

iv Acknowledgments

I would like to express my sincerest gratitude to my advisor, Dr. Jenifer Locke, for her kind financial support and patient guidance throughout the course of my PhD career. She taught me how to think logically and solve problems with rigorous scientific approaches. I also acknowledge her patience and support during the completion of this document. I especially would like to thank Dr. Frankel for allowing me to have the opportunity to join the Fontana Corrosion Center. Furthermore, I would also like to thank

Dr. Christopher Taylor and Dr. Glenn Daehn for agreeing to serve on my thesis committee and for advices they kindly provided.

I would also like to thank current and past FCC members who helped me during the course of my PhD work, including Dr. Jay Srinivasan, Dr. Leslie Bland, Dr, Yakun

Zhu, Dr. Xiaolei Guo, Dr. Saba Navabzadeh, Dr. David Schrock, Jackson Pope, Sara

Cantonwine, Dr. Shanshan Wang, Dr. Belinda Hurley, Dr. Mai Weijie, Brandon Free,

Anup Panindre, Paul Krell, Kuo-Hsiang Chang, and the rest of the group. I also acknowledge the help of Dr. Xi Wang, Dr. Angie Huggins Gonzalez, and Dr. Aline

Gabbardo with sample preparation. I would also like to extend my thanks to Ms. May

Wang and Mr. Mark Cooper for all of the administrative and logistic support.

v Last but not least, I would like to thank my whole family for their unconditional love and support during the course of this work. would like to extend a specific thank to my wife,

Wensi, for all of her love, encouragement, and sacrifice.

Finally, I would like to acknowledge the Center for Design and Manufacturing

Excellence (CDME) at The Ohio State University with financial support from Economic

Development Administration, Department of Commerce.

vi Vita

2014…………………………………… B.S., Materials Science and Engineering, The Ohio State University, Columbus, OH 2017…………………………………… M.S., Materials Science and Engineering, The Ohio State University, Columbus, OH 2014 – Present………………………… Ph.D., Materials Science and Engineering, The Ohio State University, Columbus, OH

Publications

1. Dadi Zhang, Jayendran Srinivasan, Jenifer S. (Warner) Locke, “Assessment of the

Ability of Laboratory Accelerated Corrosion Tests to Accurately Predict On-road

Corrosion of 6xxx Al Alloys”, In preparation.

2. Dadi Zhang, Jenifer S. (Warner) Locke, “Effects of Microstructure on Corrosion

Behavior of Al-Mg-Si Alloys in Accelerated Tests”, Planned.

Fields of Study

Major Field: Materials Science and Engineering

vii Table of Contents

Abstract ...... ii Acknowledgments ...... v Vita ...... vii Table of Contents ...... viii List of Tables...... xi List of Figures ...... xii 1. Introduction and Objectives ...... 1 1.1 Corrosion Behavior of Al-Mg-Si Alloys in Field and Laboratory Exposure ...... 2 1.1.1 Automotive Environment – Deicing Agents ...... 2 1.1.2 In-service and Outdoor Exposure ...... 5 1.1.3 Objective A ...... 8 1.1.4 Accelerated Tests ...... 8 1.1.5 Correlation of Laboratory and Field Exposure ...... 12 1.1.6 Objective B ...... 15 1.2 Microstructure and Localized Corrosion of Al-Mg-Si Alloys ...... 16 1.2.1 Pitting and Intergranular Corrosion ...... 16 1.2.2 Pitting Induced by Constituent Particle Cluster ...... 19 1.2.3 Objective C ...... 20 2. Corrosion Behavior of Automotive Al-Mg-Si alloys During Field Exposure ...... 22 2.1 Abstract ...... 22 2.2 Objectives ...... 22 2.3 Experimental Procedure ...... 24 2.3.1 Materials ...... 24 2.3.2 Field Exposure ...... 25 2.3.3 Post-test Analysis ...... 26 2.4 Results ...... 28 viii 2.4.1 Grain Structure ...... 28 2.4.2. Corrosion Morphology ...... 28 2.4.3 Cross-sectional Analysis ...... 31 2.5 Discussion ...... 33 2.6 Conclusion ...... 36 3. Corrosion Behavior of Al-Mg-Si Alloy in Accelerated Tests ...... 54 3.1 Abstract ...... 54 3.2 Objectives ...... 55 3.3 Experimental ...... 56 3.3.1 Laboratory Corrosion Tests ...... 56 3.3.2 Methods to Quantify the Similarity in Corrosion Morphology between Field Exposure and Laboratory Accelerated Corrosion Tests ...... 59 3.4 Results ...... 64 3.4.1 Corrosion Morphology in Laboratory Corrosion Tests ...... 64 3.4.2 Corrosion Attack Depth ...... 70 3.4.3 Quantified Assessment of Correlation of Corrosion Morphology ...... 71 3.5 Discussion ...... 76 3.5.1 Ability of Laboratory Tests to Match Field Exposure ...... 76 3.5.2 Corrosion Morphology and Test Condition...... 82 3.6 Conclusions ...... 88 4. Effects of Microstructure and Electrochemical Property on Corrosion Behavior of Al- Mg-Si Alloys ...... 122 4.1 Abstract ...... 122 4.2 Objectives ...... 123 4.3 Experimental Procedure ...... 124 4.3.1 Metallurgical Characterization ...... 124 4.3.2 Optical Profilometry ...... 125 4.3.3 Electrochemical Characterization ...... 126 4.3.4 Accelerated Corrosion Testing of 6061-T6’ ...... 130 4.4 Results ...... 131 4.4.1 Characterization of Intermetallic Particles ...... 131 4.4.2 Characterization of Shallow Pitting using Optical Profilometry ...... 133 4.4.3 Electrochemical Characterization ...... 134 4.4.5 Corrosion morphology of 6061-T6’ in Laboratory Corrosion Tests ...... 137 ix 4.5 Discussion ...... 137 4.5.1 IGC from Precipitates and Precipitate Free Zones at Grain Boundaries ...... 138 4.5.2 Trenching and Pitting from Coarse Fe-rich IMPs ...... 140 4.5.3 Proposed Corrosion Mechanisms in Exposure ...... 143 4.5.4 Connection between Microstructure and Electrochemistry and Correlation between Field and Lab Testing ...... 145 4.6 Conclusions ...... 146 5. Conclusions and Impact ...... 171 5.1 Conclusions ...... 171 5.2 Technological Impacts ...... 173 6. Future Work ...... 174 References ...... 176 Appendix A. Matlab Codes ...... 184

x List of Tables

Table 1.1: Summary of corrosion forms reported in the relevant literature after long-term exposure tests. All the specimens were phosphated and coated. Scribes were applied on the coating to expose bare metal. The references are listed in the table...... 21 Table 2.1: Alloy compositions in wt.%. Measured by ICP/OES. Si content was determined by a gravimetric method...... 38 Table 2.2: Fitting parameters and adjusted R-squares of power-law fits to the attack depth data shown in Figure 2.16...... 38 Table 3.1: A comparison of key features of each accelerated test used in this study. Any solution additives besides NaCl is given in the appropriate column...... 90 Table 3.2: Summary of corrosion morphology in laboratory corrosion tests and field exposure. Corrosion morphology observed after 2-year field exposure is also classified based on the result from Chapter 2. Boxes indicate alloys with similar corrosion behavior...... 91 Table 3.3: Fitting parameters and adjusted R2 for power-law fits (depth = Ctm) of average maximum depth of attack, depth (µm), as a function of time, t (hours)...... 92 Table 4.1: Cluster depth with m = 1, 3, and 5 in µm at the ~95th percentile...... 147 Table 4.2: Pitting potential Ep measured in deaerated 5 wt.% NaCl solution and open circuit potential in quiescent solutions of G110 (1 M NaCl + H2O2), B117 (0.9 M NaCl), G85-A2 (0.9 M NaCl + pH 3), CASS (0.9 M NaCl + Cu2+ + pH 3), and GMW (0.2 M - [Cl ] + NaHCO3). Unit: mV vs. SCE...... 147 Table 4.3: Corrosion current density, icorr, measured in quiescent solutions of G110 (1 M NaCl + H2O2), B117 (0.9 M NaCl), G85-A2 (0.9 M NaCl + pH 3), CASS (0.9 M NaCl + 2+ - 2 Cu + pH 3), and GMW (0.2 M [Cl ] + NaHCO3). Unit: 10e-6 A/cm ...... 148 Table 4.4: Density of Fe-rich IMPs measured on SEM images and the percentage of area occupied by IMPs...... 148 Table 4.5: Proposed corrosion pathways and corresponding alloys and test environment (including results from Chapter 2 and 3)...... 149

xi List of Figures

Figure 2.1: Location of the exposure rack at the bottom of a CABS bus and sample arrangement on the rack. Red arrow indicates the direction of observation for the image below...... 39 Figure 2.2: Schematic of a sample coupon and the location of the cross-section for cross- sectional analysis. The dark grey section of the coupon was cut and mounted for cross- sectional analysis. The red cross-section was used. The arrow indicates direction of observation...... 40 Figure 2.3: Example of total attack depth measurement...... 40 Figure 2.4: Optical micrographs of the grain structure of the tested alloys. (a) 6022-T4; (b) 6022-PB; (c) 6061-T4; (d) 6061-T6; (e) C26N-PB. Longitudinal direction (L) represents the rolling direction. The short transverse (S) direction encompasses the full sheet thickness. The length of scale bars is 200 µm...... 41 Figure 2.5: Exposed coupon surface of 6022-T4 after cleaning: a) 3 months; b) 5 months; c) 15 months; d) 21 months. (Each image shows a 2 in by 2 in area of the coupon.) ...... 42 Figure 2.6: SEM and a magnified view of the boxed area of a representative site of attack on 6022-T4 after a-b)3 months, c-d) 5 months, e-f) 15 months, and g-h) 21 months of field exposure...... 43 Figure 2.7: Exposed coupon surface of 6022-PB after cleaning: a) 3 months; b) 5 months; c) 15 months; d) 21 months. (Each image shows a 2 in by 2 in area of the coupon.) ...... 44 Figure 2.8: SEM and a magnified view of the boxed area of a representative site of attack on 6022-PB after a-b)3 months, c-d) 5 months, e-f) 15 months, and g-h) 21months of field exposure...... 45 Figure 2.9: Exposed coupon surface of 6061-T4 after cleaning: a) 3 months; b) 5 months; c) 15 months; d) 21 months. (Each image shows a 2 in by 2 in area of the coupon.) ...... 46 Figure 2.10: SEM and a magnified view of the boxed area of a representative site of attack on 6061-T4 after a-b)3 months, c-d) 5 months, e-f) 15 months, and g-h) 21months of field exposure. The arrows are pointing to IGC fissures...... 47 Figure 2.11: Exposed coupon surface of 6061-T6 after cleaning: a) 3 months; b) 5 months; c) 15 months; d) 21 months. (Each image shows a 2 in by 2 in area of the coupon.) ...... 48 Figure 2.12: SEM and a magnified view of the boxed area of a representative site of attack on 6061-T6 after a-b)3 months, c-d) 5 months, e-f) 15 months, and g-h) 21months of field exposure...... 49 Figure 2.13: Exposed coupon surface of C26N-PB after cleaning: a) 3 months; b) 5 months; c) 15 months; d) 21 months. (Each image shows a 2 in by 2 in area of the coupon.) ...... 50 xii Figure 2.14: SEM and a magnified view of the boxed area of a representative site of attack on C26N-PB after a-b)3 months, c-d) 5 months, e-f) 15 months, and g-h) 21months of field exposure...... 51 Figure 2.15: Optical microscopy images of cross-section of localized attack after 21 months of exposure for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N- PB. The inner images are a magnified view of the boxed areas...... 52 Figure 2.16: Average maximum depth of localized attack measured on cross-sectional images in field exposure for 3, 5, 15, and 21 months. The maximum depth is calculated using the average of the five deepest sites. Error bars indicate one standard deviation from the average...... 53 Figure 3.1: Test setup for ASTM G110...... 93 Figure 3.2: Coupon arrangement in the salt spray chamber...... 93 Figure 3.3: Modified GMW14872 flow diagram...... 94 Figure 3.4: Example of identification of individual localized attack sites...... 95 Figure 3.5: Example of an a) original cropped image and the resulting images after image processing of b) binarization, c) edge detection, and d) resized...... 95 Figure 3.6: Example of the resulting images of boxes covering the corrosion boundaries using different box sizes: a) box size = 1 pixel, N = 7695; b) box size = 2 pixels, N = 3980; c) box size = 4 pixels, N = 1972; d) box size = 8 pixels, N = 876; e) box size = 16 pixels, N = 327; f) box size = 32 pixels, N = 110...... 96 Figure 3.7: Log-log plot of the number of boxes vs. the box size...... 96 Figure 3.8: Acquisition of input images for fine-tuning of GoogLeNet...... 97 Figure 3.9: Transfer learning of GoogLeNet: a) original GoogLeNet model; b) retaining; c) classification using re-trained GoogLeNet...... 98 Figure 3.10: Optical micrographs showing 4 categories for localized attack observed on cross-sections after exposure: a) shallow pitting; b-c) larger-scale pitting; d) isolated IGC; e-f) pit-associated IGC...... 99 Figure 3.11: Coupon surface after 24-hour exposure of ASTM G110 test for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N-PB...... 100 Figure 3.12: Optical microscopy images of cross-section after 24-hour exposure of ASTM G110 test for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6 with a larger-scale pit and pit-associated IGC, e) 6061-T6 with isolated IGC and shallow pits, f) C26N-PB with larger-scale pits, isolated IGC, and pit-associated IGC, and g) C26N-PB with shallow pits...... 101 Figure 3.13: Coupon surface and top-view optical micrographs after 30-day exposure of ASTM B117 test for a) 6022-T4, b)6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N-PB...... 102 Figure 3.14: Representative optical microscopy images of cross-sections after ASTM B117 test for 2, 7, 14, and 30 days...... 103 Figure 3.15: Coupon surface and top-view optical micrographs after 60-day exposure of cyclic B117 test for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N-PB...... 104 Figure 3.16: Optical microscopy images of cross-section after 60-day exposure of cyclic B117 test for a) 6022-T4, b)6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N-PB. f) IGC in 6061-T6 after 7 days of exposure in cyclic B117...... 105 xiii Figure 3.17: Coupon surface and top-view optical micrographs after 54-cycle exposure of GMW14872 test for a) 6022-T4, b) 6061-T6, and c) C26N-PB...... 106 Figure 3.18: Representative optical microscopy images of cross-sections after GMW14872 test for 2, 7, 14, and 54 cycles...... 107 Figure 3.19: Coupon surface and top-view optical micrographs after 44-hour exposure of CASS test for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N-PB...... 108 Figure 3.20: Representative optical microscopy images of cross-sections after CASS test for 8, 22, and 44 hours...... 109 Figure 3.21: Coupon surface and top-view optical micrographs after 30-day exposure of ASTM G85-A2 test for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N- PB...... 110 Figure 3.22: Representative optical microscopy images of cross-sections after ASTM G85-A2 test for 2, 7, 14, and 30 days...... 111 Figure 3.23: Average maximum depth of attack that is greater than 10 μm as measured from cross-section images for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N-PB in ASTM G110, ASTM B117, ASTM G85-A2, and CASS. The lines are power-law fits to data from individual test environments where green triangles are for ASTM G85-A2, red circles indicate CASS, blue upside-down triangles indicate ASTM B117, cyan diamonds indicate ASTM G110, and black squares indicate field behavior. Field exposure results are replotted from Chapter 2 (Figure 2.16). The errors bars on all data points show one standard deviation from the average values...... 112 Figure 3.24: Fractal dimension (FD) of corrosion front estimated using box-counting method for (a-b) 6022-T4, (c-d) 6061-T4, and (e-f) 6061-T6. The plots are the mean value of FD is plotted with the tips of the error bar indicating the maximum and minimum in the plots on the left. The arrows indicate the corresponding exposure times for the cumulative probability plot. On the right are cumulative probability of FD for the selected times of exposure...... 113 Figure 3.25: Length/area ratio, L/A, of corrosion front estimated using box-counting method for (a-b) 6022-T4, (c-d) 6061-T4, and (e-f) 6061-T6. The plots are the mean value of L/A is plotted with the tips of the error bar indicating the maximum and minimum in the plots on the left. The arrows indicate the corresponding exposure times for the cumulative probability plot. On the right are cumulative probability of L/A for the selected times of exposure...... 114 Figure 3.26: Length/area ratio, L2/A, of corrosion front estimated using box-counting method for (a-b) 6022-T4, (c-d) 6061-T4, and (e-f) 6061-T6. The plots are the mean value of L2/A is plotted with the tips of the error bar indicating the maximum and minimum in the plots on the left. The arrows indicate the corresponding exposure times for the cumulative probability plot. On the right are cumulative probability of L2/A for the selected times of exposure...... 115 Figure 3.27: Confidence of correlation (probability) of corrosion morphology in field exposure to corrosion tests for 6022-T4. Confidences for each sample (cross-section) were plotted as columns in the corresponding subplots...... 116 Figure 3.28: Confidence of correlation (probability) of corrosion morphology in field exposure to corrosion tests for 6061-T4. Confidences for each sample (cross-section) were plotted as columns in the corresponding subplots...... 116 xiv Figure 3.29: Confidence of correlation (probability) of corrosion morphology in field exposure to corrosion tests for 6061-T6. Confidences for each sample (cross-section) were plotted as columns in the corresponding subplots...... 117 Figure 3.30: Correlation of FD and L2/A to corrosion morphology in 6022-T4 after accelerated tests and field exposure...... 118 Figure 3.31: Correlation of FD and L2/A to corrosion morphology in 6061-T4 after accelerated tests and field exposure...... 119 Figure 3.32: Correlation of FD and L2/A to corrosion morphology in 6061-T6 after accelerated tests and field exposure...... 120 Figure 3.33: SEM of 6061-T6 after 7 days of exposure in ASTM B117...... 121 Figure 4.1: Process for particle cluster analysis. a) Backscattered SEM image of the L x S surface. b) particle identified through brightness difference. c) Fit ellipses of IMPs. .... 150 Figure 4.2: Modeled solidification curves for a) Al5FeSi and b) Q-Al4Cu2Mg8Si7 using Thermo-Calc Software TCAL6: TCS Aluminum-based Alloys Database...... 151 Figure 4.3: Experiment setup for potentiodynamic polarization using electrochemical microcell...... 152 Figure 4.4: Backscattered SEM images of cast surrogates of a) Al5FeSi and b) Q- Al4Cu2Mg8Si7. Atomic weight of elements is shown in the tables...... 153 Figure 4.5: Optical micrograph of cast surrogate of a) Al75(Fe13MnCr)Si10 and b) Q phase after anodic polarization with microcell. The exposed area is indicated by the red circle...... 153 Figure 4.6: Backscattered SEM of the L X T surface of a) 6022-T4, b) 6061-T4, c) 6061- T6, and d) C26N-PB. Accelerating voltage 20 kV. Spot size 5. Scale bar: 400 µm...... 154 Figure 4.7: Magnified view of IMPs on the L x T surface of a) 6022-T4, b) 6061-T4, c) 6061-T6, and d) C26N-PB. Atomic weight of elements in the particles were determined by EDS and given in the table below the images. The accelerating voltage and spot size were adjusted to optimize the image quality...... 155 Figure 4.8: Cumulative probability of the height of clusters in the short transverse direction of a) 6022-T4, b) 6061-T4, c) 6061-T6, and d) C26N-PB with different multiplication factor. The original fit ellipses were used for m = 1...... 156 Figure 4.9: Relative probability (column) and cumulative probability (curve) of pit depth (a-b) and area (c-d) of 6022-T4 measured by OP after 2 days and 30 days of exposure in ASTM B117...... 157 Figure 4.10: Relative probability (column) and cumulative probability (curve) of pit depth (a-b) and area (c-d) of 6061-T6 measured by OP after 2 days and 30 days of exposure in ASTM B117...... 158 Figure 4.11: Relative probability (column) and cumulative probability (curve) of pit depth (a-b) and area (c-d) of C26N-PB measured by OP after 2 days and 30 days of exposure in ASTM B117...... 159 Figure 4.12: Relative probability (column) and cumulative probability (curve) of pit depth (a-b) and area (c-d) of 6022-T4 measured by OP after 14 days and 54 days of exposure in GMW 14872...... 160 Figure 4.13: Relative probability (column) and cumulative probability (curve) of pit depth (a-b) and area (c-d) of 6061-T6 measured by OP after 14 days and 54 days of exposure in GMW 14872...... 161 xv Figure 4.14: Relative probability (column) and cumulative probability (curve) of pit depth (a-b) and area (c-d) of C26N-PB measured by OP after 14 days and 54 days of exposure in GMW 14872...... 162 Figure 4.15: Potentiodynamic polarization curves in the corrosion test solutions and cyclic polarization curve in deaerated 5 wt.% (0.9 M) NaCl solution for 6022-T4. The corresponding corrosion test are ASTM B117 (0.9 M NaCl), ASTM G85-A2 (0.9 M 2+ NaCl + pH 3), CASS (0.9 M NaCl + Cu + pH 3), ASTM G110 (1 M NaCl + H2O2), and - GMW14872 (0.2 M [Cl ] + NaHCO3)...... 163 Figure 4.16: Potentiodynamic polarization curves in the corrosion test solutions and cyclic polarization curve in deaerated 5 wt.% (0.9 M) NaCl solution for C26N-PB. The corresponding corrosion test are ASTM B117 (0.9 M NaCl), ASTM G85-A2 (0.9 M 2+ NaCl + pH 3), CASS (0.9 M NaCl + Cu + pH 3), ASTM G110 (1 M NaCl + H2O2), and - GMW14872 (0.2 M [Cl ] + NaHCO3)...... 163 Figure 4.17: Potentiodynamic polarization curves in the corrosion test solutions and cyclic polarization curve in deaerated 5 wt.% (0.9 M) NaCl solution for 6061-T4. The corresponding corrosion test are ASTM B117 (0.9 M NaCl), ASTM G85-A2 (0.9 M 2+ NaCl + pH 3), CASS (0.9 M NaCl + Cu + pH 3), ASTM G110 (1 M NaCl + H2O2), and - GMW14872 (0.2 M [Cl ] + NaHCO3)...... 164 Figure 4.18: Potentiodynamic polarization curves in the corrosion test solutions and cyclic polarization curve in deaerated 5 wt.% (0.9 M) NaCl solution for 6061-T6. The corresponding corrosion test are ASTM B117 (0.9 M NaCl), ASTM G85-A2 (0.9 M 2+ NaCl + pH 3), CASS (0.9 M NaCl + Cu + pH 3), ASTM G110 (1 M NaCl + H2O2), and - GMW14872 (0.2 M [Cl ] + NaHCO3)...... 164 Figure 4.19: Representative potentiodynamic polarization curves measured on cast surrogates of second phases and pure Al in a) 5 wt.% NaCl, b) 5 wt.% NaCl at pH3, and c) mixed salt solution of 0.9 wt.% NaCl, 0.1 wt.% CaCl2, and 0.075 wt.% NaHCO3. .. 165 Figure 4.20: Plot of the measured corrosion potential and corrosion current density of cast surrogates of second phases and pure Al in a) 5 wt.% NaCl, b) 5 wt.% NaCl at pH=3, and c) mixed salt solution of 0.9 wt.% NaCl, 0.1 wt.% CaCl2, and 0.075 wt.% NaHCO3. All values were taken from polarization curves measured using the microcell. The standard deviation is indicated by the error bars...... 166 Figure 4.21: Optical micrograph of cross-section of 6061-T6’ after exposure in a) ASTM G110 for 24 hours, b) ASTM B117 for 30 days, c) ASTM G85-A2 for 30 days, d) CASS for 44 hours...... 167 Figure 4.22: Cumulative probability of pit depth in ASTM B117 and cluster height with certain throwing power, m. Black lines are depths of shallow pits after 30 days of ASTM B117 measured by OP. Red lines are cluster height with certain throwing power for corresponding alloys...... 167 Figure 4.23: Schematic of formation of particle clusters depending on particle density and throwing power...... 168 Figure 4.24: Cumulative probability of the area of IMPs and pit area after ASTM B117 (30 days) and GMW14872 (54 cycles) of a) 6022-T4 and b) 6061-T6...... 169 Figure 4.25: Schematic of relationship between microstructural features and corrosion types...... 170

xvi 1. Introduction and Objectives

In recent decades, there has been a high driving force to reduce energy consumption and transportation cost by reducing weight of vehicles. A 10% reduction in weight has been shown to generally provide an improvement of 6% to 8% in fuel economy [2]. Lightweight materials can also contribute to accelerating the replacement of conventional vehicles by improving the competitiveness of electric vehicles [2]. Among the lightweight materials of interest, wrought and extruded products of heat-treatable

6XXX (Al-Mg-Si) Al alloys are considered to be competitive substitutes for body-in- white applications and inner and outer panels [3]. Despite the fact that Al-Mg-Si alloys have relatively good corrosion resistance, their susceptibility to localized corrosion has been reported in the literature, especially when aggressive ions like chloride are present

[4-11]. For materials in automotive applications, it is critical to maintain mechanical and good exterior appearance for many years in service; therefore corrosion resistance of any new materials needs to be evaluated before heavy commercial use.

There are two primary methods to evaluate long-term corrosion resistance of a material in the automotive industry. The intuitive way is to expose the material to an in- service environment and inspect after some time. Although this method would provide the most reliable and practical results, it may take a long time (tens of years) before the failure of a material that has good corrosion resistance (e.g. aluminum alloys) is

1 observed. Therefore, in order to evaluate corrosion resistance of any material in a relatively short period, it is necessary to utilize a faster way to estimate corrosion resistance in long-term. In the automotive industry, accelerated salt spray testing is a common way to address this problem. However, there is debate over how well the accelerated tests can simulate corrosion of the in-service environment and predict long- time corrosion behavior. This lack of consensus has resulted in the development of several standards and procedures that may cause more controversy in evaluation of corrosion performance in service. The goal of this work is to address the knowledge gap between corrosion behavior in accelerating testing and in-service exposure by choosing accelerated tests based on their correlation to actual in-service corrosion, and by assessing the results of such tests in terms of the commonly accepted mechanisms of localized corrosion of heat-treatable Al alloys.

1.1 Corrosion Behavior of Al-Mg-Si Alloys in Field and Laboratory Exposure

1.1.1 Automotive Environment – Deicing Agents

The growing usage of chloride-based ice control products followed by abrasives increases the risk of corrosion on metals in vehicles [12-14]. The Western Transportation

Institute [12] conducted an investigation on the corrosion of trucks operating on Montana highways, where chloride deicers were in use. Significant crevice corrosion was found to have occurred between the truck winch and frame where the winch was attached, whereas filiform corrosion was observed under the coating near frame corners and on brake chambers [12]. Other similar surveys conducted in Maine and Colorado also reported that 2 the use of deicing salts could lead to severe atmospheric corrosion primarily on the electrical and brake systems of a vehicle [13, 15]. The types of corrosion observed also depend on the material exposed to the aggressive environment [16]. According to a survey among various Departments of Transportation (DOTs), 81.3% of the survey respondents reported that cast irons were found to corrode followed by carbon steels

(73.5%), composites (68.8%) and magnesium alloys (68.2%) [16]. On the other hand, stainless steels and aluminum alloys were susceptible to severe localized corrosion (50%)

[16]. Although some of the commercial deicing agents contain corrosion inhibitors, there is limited evidence on their effectiveness in protecting road infrastructure or exposed metals of vehicles, because both the deicing chemicals and metals can vary from case to case, and most of the corrosion inhibitors are environment-specific in their effectiveness

[13].

Among the ice-control agents currently used all over the world, chloride salts continue to be the most widely used [17-20]. Sodium chloride is the most commonly used deicing chemical in the forms of dry salt, prewetted salt, and brine; and it is typically regarded as the most cost-effective [18, 21-24]. Calcium and magnesium chloride are less used, but can be mixed with sodium chloride for low-temperature application (< -21°C)

[12]. Typical snow and ice control operations involving deicing chemicals include anti- icing before snow events and deicing after snow events [25]. Anti-icing is a general strategy that hinders the formation of ice/pavement bond by the application of ice control chemicals before a snow event [25]. According to a survey conducted in 2003-2004, 15 of 16 states applied multiple pretreatments during winter [23]. All of the DOTs surveyed applied pretreatment multiple times during a winter season depending on protocol or 3 conditions [23]. Most states used sodium chloride as their primary chemicals for pretreatment, while mountainous western states and Alaska preferred to use magnesium chloride [26]. There were also instances of states using mixtures of various chemicals.

For instance, Utah blended salt brine with potassium acetate and magnesium chloride for better performance [23]. The application rates varied between 15 and 50 gallons per line mile (gplm) for sodium chloride, while the application rates of magnesium chloride varied from 10 to 35 gplm [23]. The concentration of salt brine for anti-icing operations varied for different agencies, but usually a 23 wt% NaCl solution was used [23]. In general, northeastern states together accounted for more than 85 percent of all road salt used nationally [27].

The residual concentration of salt brine can be determined by using a Boschung

Megatronic instrument (SOBO-20), which measures the temperature-compensated conductivity between two sites on the road surface and calculates the mass per unit area of salt [22]. With an application rate of NaCl brine at about 40 gplm, the initial concentration was found to be 98 g/m2, followed by a rapid exponential decay to 2~6 g/m2 depending on the porosity of pavement [22]. Regarding this exponential decay,

Blomqvist and Gustafsson [19] proposed a model that describes the exponential relationship between traffic and concentration of residual salt in the wheel track:

푅푆 = 푆 ∙ 푒−푘∙푃퐶 where RS = residual salt, S = initial amount of salt applied, PC = the number of private car equivalents (truck & bus =5, semi = 7), and k is an empirically determined coefficient. Even after snow and ice on the roadway melt and dry out, a significant amount of deicing salt aerosols can be deposited on vehicles and adjacent areas by a 4 “washout” process [28]. According to the washout mechanism, suspended particles of deicing salts are collected by snow or raindrops and are deposited on the roadway and on vehicles [28]. Dried (crystallized) salt on the roadway can be aerosolized by moving vehicles and transported consequently by wind [24]. Williams et al.[28] reported that the

NaCl concentration in total suspended particles increased from about 1 g/m3 to peak values of more than 10 g/m3 after snow events due to high-speed highway traffic. Those airborne particles could travel deeper into a vehicle than splash. Therefore, both external and internal components of vehicles are susceptible to corrosion attack when exposed to a corrosive environment resulting from a very high deposition rate of NaCl due to splash or aerosolized salt particles.

1.1.2 In-service and Outdoor Exposure

The rate of mass loss during exposure is a commonly-used simple method to evaluate the corrosion resistance of metals. Prior research has shown that the mass-loss rate of Al alloys in outdoor exposure follows the power law [29-33], one of which can be described as the following function [29]:

M = K푡푛 where M is the mass loss (g/cm2), K is the mass loss in the first year of exposure, and n is a fitted parameter. The mass loss exponent, n, has been found to vary from 0.5 to 1 for

AA6XXX alloys in outdoor exposure [29, 34]. It has been proposed that the corrosion rate of aluminum alloys would be suppressed after several months of exposure due to a protective corrosion product film controlled by the diffusion process of corrosion [35].

5 The power law relationship mentioned above does not take environmental factors into consideration. The corrosion rate of aluminum was found to be mostly associated with time of wetness and chloride and SO2 deposition by fitting mass loss data to environmental factors with a linear function [32, 33, 36, 37], shown as an example for pure aluminum in outdoor exposure in the following equation (R= 0.77) [32]:

퐴 = 0.13 + 2.19퐶푙(1 − 0.57푡푤 − 0.015푇) + 0.57푆 where A is the annual corrosion (µm), tw is the time of wetness (annual fraction of

RH>80%, T>0 ˚C), T is the average annual temperature (˚C), and S and Cl are the annual

- 2 average deposition rate of SO2 and Cl (mg/m per day), respectively. Interactions between the environmental factors were also introduced by adding the products of each two factors. The terms that had a negligible effect on the correlation coefficient were eliminated from the equation.

In outdoor exposure, tested panels are usually placed on sample racks with a certain angle to the horizontal plane. Sample orientation is of particular interest because it can be associated with environmental factors, such as time of wetness and salt deposition rate. However, no widely accepted conclusion has been made to this end in the studies that have considered these effects [29, 38, 39]. It has been observed that, with a 30° angle in an industrial atmosphere, the skyward surface generally suffered more severe pitting attack than the groundward side of AA6005-T5 [29]. Panels of an Al-Cu alloy exposed to a heavy tropical marine atmosphere for 3 months with an angle of 45˚ were to found to have significantly more severe pitting on the bottom side than the top side in terms of both the depth and spread [38]. On the other hand, an outdoor exposure performed on AA

1050 in a marine and industrial atmosphere showed that the upper surface was more 6 susceptible to pitting attack in the presence of chloride ions, even with small deposition rate [39].

Susceptibility to localized corrosion was reported by previous research on Al alloys in long-term outdoor exposure [29, 34-36, 40, 41]. Pitting was seen to be the dominating corrosion type during outdoor exposure in a marine atmosphere or at high levels of salt deposition [34, 41]. Pitting attack in outdoor exposure developed quickly, but the rate of penetration is stabilized to a relatively low value due to repassivation enhanced by corrosion product formation [35, 39, 42, 43]. The average and maximum pitting depth on AA-1050 can be fitted to a logarithmic function of time during a 4-year exposure to a marine atmosphere [39]. In contrast, pitting density generally increased linearly with time and the slope increased with higher chloride contamination [39]. In a six-year outdoor exposure of Al-Mg-Si alloys to a marine atmosphere, the penetration depth of pitting reached about 0.002 inch in the first two years - while small or no increase of penetration was observed in the following year [35]. For an industrial environment where SO2 was present, the depth of pitting continuously increased during the exposure [35].

In addition to pitting, intergranular corrosion (IGC) was also found in outdoor exposure [29, 40, 44]. Dean et al. [29] reported that IGC penetrated to a depth of 2 to 3 grains in the recrystallized layer on top of AA6005-T5 after a ten-year exposure to an industrial and a marine atmosphere. In the road exposure test conducted by Bauger and

Furu [40], peak-aged 6XXX-alloys with uniaxial recrystallized grains and as low as 0.01 wt% Cu content were susceptible to IGC, while pitting was found to dominate without recrystallized grains. Hasegawa et al. [44] studied corrosion performance of painted AA 7 6009-T4 in a 2-year road exposure in Japan. After removal of the paint film and corrosion product, localized corrosion in this alloy was found to be a combination of pitting and

IGC [44].

1.1.3 Objective A

Corrosion morphology and severity of 6XXX Al alloys depends on alloy type and environment for exposure [29, 34-36, 40, 41]. The first objective of this work is to identify the corrosion behavior of Al-Mg-Si during field exposure in order to compare with that in laboratory accelerated tests. Specifically, the corrosion morphology during field exposure will be investigated by cross-sectioning and SEM. Al-Mg-Si alloys with different compositions and aging conditions are used to investigate the effect of microstructure on their corrosion behavior in field.

1.1.4 Accelerated Tests

As mentioned previously, the widespread use of chloride-based deicing agents, like NaCl, CaCl2, and MgCl2, is a major factor causing corrosion on aluminum vehicle components [17-20]. Consequently, most of the accelerated tests, such as ASTM B117

[45] and ASTM G85 [46], are based on chloride-based solutions.

A typical cyclic accelerated test involves a cyclic process of wetting of the exposed surface by deicing salt solution followed by dying stage, the latter having been found to enhance corrosion [47-49]. According to the literature on corrosion rate during wet/dry transition by measuring anodic dissolution current [50-52], the corrosion rate of 8 exposed metal first increases for a short time in the initial wetting stage when electrolyte formed on the surface as salts dissolved, and then decreased as the electrolyte became more dilute. During the drying stage, since the dominating cathodic reaction is oxygen reduction, the corrosion rate increased as a result of a concentrated electrolyte and enhanced diffusion of oxygen through a thinner electrolyte layer [50-52]. As drying proceeded, corrosion ceased eventually due to the lack of electrolyte presence [50-52].

Research on Al alloys under thin electrolyte layer is limited, but evolution of the aluminum corrosion rate under an NaCl droplet was also found to follow this pattern

[53]. Even though wet/dry cycles can lead to higher corrosion attack, the dissolution rate was also found to decrease with an increasing number of cycles possibly due to a denser barrier preventing corrosion due to multiple active/passive transitions or accumulation of corrosion products [48, 54].

Limited research has been conducted to investigate the effects of different chloride salts on corrosion of automotive alloys [55-59]. The GMW 9540P(B) cyclic test that uses a mixed salt solution (NaCl, NaHCO3, and CaCl2) caused larger galvanic corrosion of an AA6XXX Al alloy coupled to steel than a test using a simple NaCl solution [58]. The effect of calcium ions is not very clear, but bicarbonate was shown in

[59] to suppress pitting attack by neutralizing the acidified electrolyte in pits.

Other research has found that CaCl2 and MgCl2 are generally considered to be less corrosive than NaCl under immersion in a bulk electrolyte, but can lead to greater mass loss in a humid environment likely due to their hygroscopic nature that leads to longer time of wetness [12, 13, 15, 60, 61]. Xi and Xie [15] conducted two typical accelerated tests, ASTM B117 [45], and SAE J2334 [62] (SAE J2334 is a commonly used cyclic test 9 in the automotive industry using mixed salt solution), on aluminum alloys. In order to evaluate the effect of different salts, the authors replaced the standard solutions with solely 1 wt% NaCl or MgCl2 solution. Mass loss measurement after the modified ASTM

B117 test [45] indicated that NaCl was more corrosive than MgCl2 on bare steels and aluminum alloys. However, SAE J2334 [62], which involves wet-dry cycles, provided the opposite result of NaCl being less corrosive than MgCl2 [15]. A modified NACE TM-

01-69 test [63] where either NaCl or MgCl2 solutions were applied on specimens by dipping into 1 wt.% and 3 wt.% concentration of each was also conducted on the same metals [64]. Similar to continuous salt spray, NaCl was more corrosive in this test [15].

With regard to CaCl2, corrosion rates of steel using the cyclic test, NACE TM-01-69 were found to be higher when using CaCl2 than NaCl brine [61].

The effects of different salts in accelerated tests can be associated with the difference in the deliquescence points, as above these RH values, deposited salt particles can absorb water in the atmosphere and create a corrosive electrolyte on the metal surface

[65, 66]. In fact, sustained corrosion was detected under NaCl and MgCl2 deposits on steel down to 33% and 11% RH, respectively, which are well below their deliquescence points [67, 68]. Both CaCl2 and MgCl2 have lower deliquescence points than NaCl, so these salts may keep the metal surface wet for a longer time during a wet/dry cycle leading to a higher corrosion rate.

Lyon et al. [48] compared the corrosion behavior of high-purity aluminum and an

Al-Mg-Si alloy during exposure to ASTM B117 [45] and a cyclic test that was simply an alternation of a high chloride containing fog or sulfate saturated fog stage and a dry stage

(heat to 35°C) for 1 hour each. The major alloying element contents in this alloy were in 10 a range of 0.62 to 0.68 wt.% for Mg, 0.22 to 0.29 wt.% for Si, and <0.5 wt.% for Cu [48].

Corrosion rate in ASTM B117 was initially higher than cyclic test and decreased with exposure time, while corrosion rate in cyclic test started low and then increased to higher values than that from ASTM B117 exposure, which reached a plateau after 8 weeks [48].

The addition of hydrogen peroxide has been found to enhance IGC in 6XXX

Alloys [69-71]. ASTM G110 is a typical immersion test used to evaluate the IGC resistance of aluminum alloys by using a NaCl solution with H2O2 [72]. An AA6005A extrusion exhibited susceptibility to both pitting and IGC in the first 30 days of exposure, followed by IGC being generally replaced by pitting that kept growing to depths of 400

µm after 210 days [69]. Burleigh et al. [70] reported that uniform corrosion was preferred on AA6013-T6 in neutral NaCl-H2O2 solution and IGC was introduced by chimneys of corrosion product that formed at sites undergoing localized attack . With regard to localized corrosion, alloy AA6013-T6 was found to be susceptible to only IGC in ASTM

G110 solution, while in an alternate immersion test using 3.5% NaCl solution, IGC tended to initiate on the walls of pits and extend to adjacent grains [71]. As a cathodic agent, hydrogen peroxide can be used to accelerate corrosion testing of Al alloys.

However, its effect on corrosion morphology needs to be investigated and correlated with in-service environment.

The seawater acidified test (SWAAT) [46], described in ASTM G85-A3, is a cyclic test that uses artificial seawater acidified by acetic acid to pH 2.8~3.0. Each cycle lasts 2 hours, involving a half-hour spray and an ensuing soak in high relative humidity at

49 ˚C [46]. This test was found to promote severe pitting corrosion and lead to different ranking of weight loss of different 6XXX alloys comparing with field test [40]. For 6xxx 11 alloys undergoing SWAAT testing, Roodbari [73] reported that the maximum pit depth generally increased logarithmically at a higher rate than the average depth for AA6061 and AA6063, and the average pit depth growth rate almost ceased after the first month of exposure. The corrosion behavior of Al-Mg-Si alloys in accelerated tests using acidified solutions has not been well correlated with field result. In order to use acidified solution to accelerate corrosion of Al alloys, the effect of low pH on corrosion morphology and growth rate needs to be investigated and compared with in-service environments.

1.1.5 Correlation of Laboratory and Field Exposure

Moran et al.[74] were among the first to evaluate how well an accelerated laboratory test could simulate in-service exposure and corrosion in their study on

AA6111-T4 with a commercial automotive coating system. This study involved outdoor exposure on in marine and industrial atmospheres, four common accelerated laboratory tests ASTM B117, ASTM G85-A2, GM9540P/B, and CCT-IV, and in-service exposure in the Great Lakes region for 1 year [74]. Because corrosion attack after the 1 year in- service exposure was too minimal, only results from outdoor exposure were used to correlate with accelerated tests [74]. The corrosion forms observed in this Moran et al. study, and several others, are summarized in Table 1.1. Linear regression analysis indicated that the test that correlated best with the outdoor seacoast exposures was ASTM

B117 in terms of both the area quantifier (damage area per unit scribe length) and morphology [74]. GM9540P/B (40 cycles) resulted in insufficient attack and no coalescence of filiform tracks [74]. CCT-IV (50 cycles) and ASTM G85-A2 (3 weeks)

12 caused an impractically severe extent and morphology of filiform corrosion were, instead of typical filament shape, uniform creepback morphology was observed in these two tests

[74].

In a study on various commercial 2XXX and 6XXX series Al alloys with coatings typically used in the automotive industry, Colvin et al. [75] argued that the ASTM G85-

A2 test correlated well with the in-service exposure on trucks operating in the Great

Lakes region, while the neutral continuous salt spray test B117 was too mild to introduce filiform corrosion on the specimens. The authors reported that blisters were often found on the filiform track of specimens exposed to the in-service environment, which was consistent with the morphology of filiform attack after the ASTM G85-A2 test [75]. With regard to extent of attack, the longest filiform tracks of ASTM G85-A2 after 2 weeks exhibited a good linear correlation with the in-service tests [75]. Bovard et al. [76] compared the filiform corrosion resistance during in-service exposure and ASTM G85-

A2 for 6XXX alloys with different surface finishes and paint systems. It was found that

ASTM G85-A2 provided the same ranking of corrosion resistance and corrosion morphology as was found after 4-year in-service exposure [76].

Another attempt was made by Courval et al. [77] to correlate accelerated lab testing and field performance of automotive aluminum alloys for up to 4 years in service.

Specimen panels of AA6111 and AA6016 were phosphated and painted with a commercial organic coating. Small isolated blisters were observed after three and four years of in-service exposure, and the intergranular penetration depth under the blister was about 60-80 µm [77]. Pitting and IGC were found to be associated with blistering and filiform corrosion in outdoor exposure of painted aluminum alloys [41, 44]. In the neutral 13 salt spray (e.g. B117) test, a series of small blisters with diameters of about 0.2-0.5 mm formed along the scribe extending to a distance of about 1 mm on both materials, and the size of the blisters did not change with exposure time [77]. Significant IGC was also observed under blisters penetrating into the metal for about 30-40 µm after 2000 hours

[77]. When the spray was acidified to a pH of ~3, similar morphology was observed but with a higher growth rate of blistering and IGC [77]. Small blisters without IGC observed underneath were found to be the dominant feature of corrosion after 80 cycles of

GM9540P/B test [77]. Adding copper to NaCl solution (copper acidified salt spray

(CASS) test [78]) caused very large pits to form instead of blisters and IGC [77].

Nakayama et al. [47] investigated the effects of pH of solution and repetition rate of wet/dry cycles on the corrosion behavior of AA6111 and AA6383 coated by commercial coating systems that involved zinc phosphating and organic painting. X- shaped scribes were applied on panels. The tests used in this study were ASTM B117,

ASTM G85-A2, and modified ASTM B117 with wet/dry cycles. It was found that the growth rate of localized attack on these alloys was higher with lower pH and/or higher wet/dry repetition rate [47]. In terms of the morphology of corrosion, typical filament shape was found in the tests with wet/dry repetition, while continuous salt spray testing

(B117) tended to generate blisters along with the scribes [47]. The B117 test also had highest correlation coefficient between the laboratory test and in-service exposure with regard to maximum extent of corrosion [47].

The environment that vehicles expose to is complicated and sometimes subtle due to numerous environment factors and interactions among them. As a result, atmospheric corrosion is usually a dynamic process due to continuously changing of temperature, 14 humidity, pollutants, etc., and exhibits various corrosion forms as given in Table 1.1.

Although many outdoor exposure and accelerated tests have been performed on 6XXX alloys, none of the tests has been announced to be able to well simulate corrosion attack in road environment in terms of both corrosion forms and propagation rate.

1.1.6 Objective B

As the correlation between accelerated corrosion tests and exposure during service in automotive environments is not definitively established, the second objective of this research is to quantify and comparatively understand the corrosion behavior of wrought Al-Mg-Si alloys in typical accelerated tests and correlate their corrosion behavior with results of field exposure. In order to investigate effects of different environmental factors on the materials, bare sheets of selected Al-Mg-Si alloys were exposed to accelerated tests including an immersion test, ASTM G110, a continuous salt spray test, ASTM B117, three cyclic tests, ASTM G85-A2, and GMW 14872 and a customized cyclic test based on ASTM B117. Effects of individual environmental factors were investigated by comparing corrosion behavior of an alloy in different tests. The final goal was to identify the critical environmental factor that determines the morphology of corrosion of the Al-Mg-Si alloys during in-service exposure and provide suggestions on the design of next-generation accelerated test for Al-Mg-Si alloys.

15 1.2 Microstructure and Localized Corrosion of Al-Mg-Si Alloys

1.2.1 Pitting and Intergranular Corrosion

Many theories have been developed to explain pitting initiation. One popular postulation for self-passivated metals, e.g. aluminum, is referred to as the film rupture mechanism, where breakdown at random weak points in the passive layer cannot be repaired spontaneously causing initiation of pits at potentials higher than the pitting potential [79, 80]. Chloride is well known as a corrosive pollutant facilitating the breakdown of the passive layer. The following reactions are involved in the propagation of pitting in an electrolyte with chloride ions [81]:

− + − 퐴푙 + 2퐻2푂 + 퐶푙 → 퐴푙(푂퐻)2퐶푙 + 2퐻 + 3푒

As production of H+ or consumption of OH- is a consequence of these reactions, pitting in aluminum is considered as an autocatalytic process due to decreased pH in the local environment. The cathodic reaction also plays an important role in corrosion of aluminum. The primary cathodic reaction in a neutral environment open to air is oxygen reduction [82]: − − 푂2 + 2퐻2푂 + 4푒 → 4푂퐻 Since the local electrolyte is acidified, in addition to the primary oxygen reduction reaction, a secondary cathodic reaction, hydrogen evolution, is also involved [82]:

+ − 2퐻 + 2푒 → 퐻2(𝑔)

In the presence of chloride ions, Al-Mg-Si alloys are susceptible to localized corrosion due to second-phase particles and microsegregations [4-6, 11, 83-88].

According to prior research, the second phase particles associated with pitting include β-

Mg2Si [11, 87] and Fe-rich intermetallic particles [4, 5, 11, 89]. Similar to other heat-

16 treatable Al alloys, Al-Mg-Si alloys are primarily precipitation-hardened by the formation of second-phase particles during aging. The main strengthening particles in these alloys are Mg2Si precipitates. A generally accepted precipitation sequence of Mg2Si precipitates in literature is SSSS → atomic clusters → GP zones → β’’ → β’ → β, where

SSSS is the supersaturated solid solution [90, 91]. Depending on the composition and aging condition, both the equilibrium β-Mg2Si phase and its metastable precursors can exist in 6XXX alloys [92]. Recent studies examining the electrochemical properties of individual phases show that the β-Mg2Si phase has a lower potential than the Al matrix, leading to the anodic dissolution of Mg in β-Mg2Si particles when exposed to aqueous electrolyte [93, 94]. Dealloying of Mg2Si occurs quickly after exposure to the electrolyte, leaving a cathodic Si-rich remnant, leading to further pitting corrosion [11].

Iron is another common element present as an impurity in all commercial Al alloys. The low solubility of transition metals in aluminum results in the formation of various intermetallic particles depending on alloy composition and thermomechanical processing route [92, 95-97]. Typical Fe-rich constituent particles include Al9Fe2Si2,

Al12Fe3Si, and Al12(FeMnCr)3Si, where Mn and Cr act as substitutions for Fe [95, 98]. A small amount of manganese and chromium are also commonly added to improve the toughness of 6XXX alloys by refining grain size and inhibiting the formation of Mg2Si precipitates at grain boundaries [99, 100]. Exposure to NaCl solutions can result in trenching from selective dissolution of the Al matrix around the Fe-rich particles [4] and enhanced cathodic kinetics [80].

Al-Mg-Si alloys usually have good resistance to intergranular corrosion (IGC) when compared to other heat-treatable alloys (2XXX and 7XXX). However, 17 susceptibility to IGC has been reported to increase with the grain boundary formation of precipitates like, β-Mg2Si and Q-phase (Al4Mg8Si7Cu2) and Cu nano-films during artificial aging, when enrichment of Cu at grain boundaries leads to the formation of a solute-depleted zone (SDZ) in the adjacent region [8, 9, 87, 88, 101-108]. As a result, the resulting potential difference between these microstructural phases can cause a selective dissolution of SDZ, which has a lower potential in comparison with the Q-phase and Cu nano-film [8]. Research has revealed that susceptibility to IGC generally increases with aging time for copper-containing alloys until sufficient over-aging raises the probability of pitting in preference to IGC due to coarsening of Cu-rich particles and discontinuity of the Cu nano-film [109].

Cu content and Si/Mg ratio are considered to be key in determining to the IGC susceptibility of 6XXX Al-Mg-Si alloys and, per the literature, IGC is expected at high

Cu content and Si/Mg ratio [8, 9, 87, 88, 106, 107]. Other types of corrosion (e.g. pitting) are more likely to occur with low Cu content and Si/Mg ratio, and a combination of pitting and IGC is found in the transition region [110]. Additional studies [101, 111] have revealed that Cu content had the greatest effect on IGC of Al-Mg-Si alloys due to the precipitation of a Cu-rich Q-phase (Al4Mg8Si7Cu2) along grain boundaries [104]. The Q- phase has a higher potential than the aluminum matrix, causing IGC by the microgalvanic couple of the Q-phase and the adjacent precipitate free zone [104]. Further study has revealed that IGC preferentially occurred in alloys that had undergone a low solution treatment temperature and high cooling rate, while pitting occurred with high treatment temperature and slow cooling rate [112]. This was attributed to the fact that high

18 treatment temperature and low cooling rate led to coarsening of Q-phase, and pitting was attributed to the coarse particles in grain boundaries [112].

1.2.2 Pitting Induced by Constituent Particle Cluster

Clusters of intermetallic particles have been found to be responsible for severe pitting in the Cu- and Zn-containing AA2024 and AA7075 alloys [113-115]. Similar to

Al-Mg-Si alloys, the initiation of localized corrosion in these alloys is associated with intermetallic particles, such as Al7Cu2Fe and α-AlFeSi, due to a potential difference between them and the Al matrix [113, 116]. An in-situ monitoring study [113] has shown that, after initiation, the isolated particles, which were far away from others, caused only pits that were relatively shallow and limited to the area around the particles [113]. On the other hand, if subsurface clusters of particles were exposed by the initial pits, severe pitting developed [113].

To describe the growth of pitting induced by clustering of constituent particles,

Harlow and Wei [117, 118] proposed a model based on galvanic corrosion of the Al matrix coupled with cathodic constituent particles. A simplified version of the time- evolution of the pit radius a due to clustering of particles is given by the following expression:

3푀퐼 1/3 푎 = [ 푝푖푡 푡 + 푎3 ] 2휋푛휌퐹 푝푐표 where M is the molecular weight; Ipit is the pitting current; n is the valence; ρ is the density; F is the Faraday constant; apco is the size of the initial cluster of particles. This

19 model assumes an ideal hemispherical pit and that Al matrix dissolution follows

Faraday’s Law.

The model outlined above was developed for AA2XXX and 7XXX series alloys.

Although localized corrosion of the 6XXX series can develop following a similar mechanism, whether the same model is applicable needs to be confirmed. Predicting the growth rate of pitting in accelerated tests or in a practical environment is likely dependent on the effect of these cathodic secondary phase particles on pitting as pit growth is a function of cathodic current.

1.2.3 Objective C

The final objective of this research is to correlate microstructure features with localized corrosion of Al-Mg-Si alloys in accelerated tests. Unlike what exists for 2xxx and 7xxx Al Alloys, the current literature lacks direct evidence of the role played by second phases particles in the pitting corrosion of 6xxx Al alloys, therefore requiring a focused study. Moreover, since the solutions used in accelerated tests contain other additions than a simple NaCl solution, it is necessary to understand the effects of these changes in chemistry on localized corrosion.

20 Table 1.1: Summary of corrosion forms reported in the relevant literature after long-term exposure tests. All the specimens were phosphated and coated. Scribes were applied on the coating to expose bare metal. The references are listed in the table.

Extent of Alloy Exposure condition Exposure time Corrosion forms Reference attack Maximum Slight filiform; Marine 2 years pitting depth= blistering; pitting. AA6009- 0.28mm [44] T4 Filiform; mixture Maximum In-service 2 years of pitting and pitting depth= IGC. 0.15mm Blister size=0.5-0.7 Small blisters; AA6111 In-service 3-4 years mm; IGC [77] IGC. depth=60-80 um Coalesced Marine 9 months filiform tracks. Industrial + NaCl AA6111- Coalesced solution applied 3 11 months [74] T4 filiform tracks. times per week In-service 1 year Negligible AA6XXX In-service Up to 6 years Filiform [76] Heavy filiform Marine 6 months corrosion AA6XXX [75] One winter Primarily In-service season blistering

21 2. Corrosion Behavior of Automotive Al-Mg-Si alloys During Field Exposure

2.1 Abstract

Corrosion behavior of three automotive wrought Al-Mg-Si alloys (6061, 6022, and C26N) was investigated during field exposure. The field exposure was conducted for up to 2 years utilizing a plastic rack affixed under a bus operating on The Ohio State

University’s campus where NaCl based deicing agents were used during wintertime.

Corrosion morphology (e.g. pitting, intergranular corrosion, and intragranular corrosion) after field exposure was investigated by SEM and cross-sectional analysis. All the tested alloys were found to be susceptible to localized corrosion. A combination of pitting and intergranular corrosion (IGC) was observed on all the tested alloys. Intra-granular corrosion tends to initiate from the IGC crevices and caused the dissolution of grains on the surface. Aging from the T4 to the T6 temper enhanced susceptibility to IGC. Pitting associated with trenching around cathodic IMP particles was seen in a limited number of cases.

2.2 Objectives

As wrought Al-Mg-Si alloys have become more popular for automotive applications, it is important to evaluate their corrosion resistance in an environment as

22 close to in-service as possible, and particularly where chloride-based deicing agents are used. Much of the previous research on outdoor exposure has focused on coated samples during exposure [47, 75-77, 119, 120], while little has been conducted to study the inherent corrosion behavior of the underlying Al substrate [31, 40]. However, the susceptibility to localized corrosion of Al-Mg-Si without coating also needs to be investigated to understand base material performance and appreciate the performance improvements that coatings may offer. The goal of the research in this chapter is to answer the following questions:

• What is the corrosion morphology of Al-Mg-Si alloys after exposure to automotive

environments that utilize de-icing agents?

• Do Al-Mg-Si alloys with different chemical compositions exhibit the same

corrosion performance during field exposure?

To answer these questions, the following objectives were set:

• Identify the corrosion morphology on Al-Mg-Si alloys after field exposure.

• Investigate the growth rate of corrosion in field exposure.

• Identify the effect of chemical composition and aging history on the corrosion

behavior of Al-Mg-Si alloys.

23 2.3 Experimental Procedure

2.3.1 Materials

The materials used in this study were rolled sheets of three commercial Al-Mg-Si alloys: AA6022, AA6061, and C26N. C26N is an Al-Mg-Si alloy composition that has yet to be registered by Arconic Inc. with the Aluminum Association. Given in Table 2.1 are the chemical compositions of alloys utilized in this work. Chemical composition was determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), while the Si content was determined by a weight-loss method. Two × two feet sheets of

6022-T4 and C26N-Paintbaked (PB) with a thickness of 1/32 inch (0.79 mm) were manufactured and provided by Arconic, Inc. The C26N sheets were received in a paint- baked condition. The paint-bake treatment of C26N-T4 was performed at 170°C for 20 minutes at Arconic to simulate the curing process of coatings on the external surface. To evaluate the effect of paint-baking on 6022, a paint-baked version, referred to here as

6022-PB, was prepared from 6022-T4 following a similar procedure to the paint-bake treatment at Arconic with a slightly longer aging time of 1 hour in order to exacerbate any microstructural change from the treatment. No coating was applied to the paint-baked samples. Two different tempers of 6061, T4 and T6, were used in this work to evaluate the effect of artificial aging. Four × four feet sheets of 6061-T6 and 6061-T4 had a thickness of 1/64 (0.40) and 1/32 inch (0.79 mm), respectively. Note that the 6061-T4 and 6061-T6 sheets were purchased separately from different lots because the T4 condition was added for investigation later in this work.

24 Grain structures of the selected alloys on the principal planes were characterized using optical microscopy. The three principal planes are defined as S × T, T × L, and L

× S, where S is the short transverse (thickness) direction, T is the long transverse (width) direction, and L is the longitudinal (rolling) direction. Specimens for each principle plane were abraded to 1200 grit using SiC paper and polished to 1 m in alcohol with diamond paste. The grain structure was then revealed by etching in Weck’s reagent with a pretreatment of 2 minutes immersion in 10 vol.% H2PO3 solution at 50 °C [121]. Grain size of each alloy was measured as the averaged linear intercept length following the intercept method described in ASTM E112 - 13 [122].

2.3.2 Field Exposure

The samples for field exposure were 2 × 4 in coupons cut from the original sheets. In order to affix samples to an exposure rack, two 1/4 in holes were drilled on the centerline near the two ends in the 4 in dimension of the coupons. Samples were affixed on the rack by using plastic screws and nuts to avoid any galvanic couples. One of the L

× T surfaces of the test coupons was abraded to 1200 grit for exposure using SiC paper in water and then cleaned in acetone using an ultrasonic cleaner. Due to scratches on some original surfaces, abraded surfaces were necessary to avoid such differences sample to sample. Prepared coupons were mounted on a perforated PVC rack horizontally placed under the bus with the abraded surface for exposure facing towards the ground. Figure

2.1 presents the arrangement of test coupons on the rack and the location of the rack

25 under the bus. All the materials of the rack in contact with the test coupons were made of plastic to avoid the effects of galvanic corrosion.

The racked samples were exposed for up to 21 months (Aug/2017 through

May/2019) on a bus operated by the Campus Area Bus Service (CABS) at The Ohio

State University (OSU). The roads on the OSU campus receive an average of 28 in of snowfall (data from NATIONAL CENTERS FOR ENVIRONMENTAL

INFORMATION) during winter when NaCl based de-icing agents (23% brine or rock salt) are used frequently. The CABS bus regularly operated around the campus area 7 days a week with less frequency on weekends. Two coupons per exposure time were collected for evaluation after 3, 5, 15 and 21 months, which were roughly before and after each peak winter season (from November through April).

2.3.3 Post-test Analysis

2.3.3.1 Scanning Electron Microscopy

Scanning electron microscopy (SEM) was performed to investigate the corrosion performance after on-vehicle exposure. Exposed coupons were cleaned in concentrated nitric acid for 5 minutes with an ultrasonic cleaner. The remaining dirt and corrosion product were further cleaned by gently scrubbing the surface with a plastic brush.

Photographic images were taken on the cleaned surface to examine the general coverage of the localized attack. SEM was conducted on a typical spot of localized attack with a beam of 20 keV and a spot size of 5 nanometers.

26 2.3.3.2 Cross-sectional Analysis

A cross-section sample with a width of about 2 cm was cut from the center region of the exposed coupon at least 1 cm away from the edges and mounted in epoxy in a way that the L x S surface was exposed. The schematic given in Figure 2.2 shows the cross- section surface (red) and the approximate location used for examination. The cross- section samples were ground to 1200 grit using SiC and polished to 1 µm diamond paste in alcohol. Optical images were taken on the cross-sections of localized attack. If no localized attack could be observed on the cross-section of a sample, the sample was abraded further for about 1 mm and polished again for optical microscopy. The process was repeated until sites of localized corrosion were observed.

The total corrosion depth of localized attack was measured on cross-sections using Fiji software [123]. An example of the total attack depth measurement is shown in

Figure 2.3. Since the corrosion attack was highly localized, individual sites of attack can be identified from the others by the unattacked regions. The depth of each individual site was measured on the cross-section, and optical micrographs of the sites with depth above

10 µm were taken for analysis. Note that the individual site of attack observed on cross- sections may not have initiated from a single event and may be a combination of two or more sites joining each other. At least two additional cross-sections with localized attack were examined after the first measurement.

27 2.4 Results

2.4.1 Grain Structure

Optical micrographs showing the etched surface of tested alloys are given in

Figure 2.4. The short transverse direction (S) in the micrographs covers the full thickness of the alloy sheets. It can be seen from the micrographs that grains in all the alloys were fully recrystallized given the nearly equiaxed nature of the grains. There is also no significant difference observed in the grain morphology along the thickness of the sheets, so the effects of changes in grain size and shape (elongation due to rolling process) due to grinding during sample preparation were assumed negligible. It can be seen that the 6061 alloys (Figure 2.4 c and d) have relatively small grains of about 20 m, while the 6022 and C26N sheets have slightly larger average grain sizes of about 40 and 30 m, respectively. No significant effect of the paint-baking treatment on the grain structure of

6022 can be observed at this level. Note that TEM was not conducted in this study to observe any effects of paintbaking on secondary phase formation and growth. SEM is reported in Chapter 4.

2.4.2. Corrosion Morphology

2.4.2.1 6022-T4 and 6022-PB

Cleaned coupon surfaces of 6022-T4 after field exposure are shown in Figure 2.5.

No evidence of uniform corrosion or large scale extreme localized corrosion, even after

21 months of exposure, was observed as indicated by the unattacked reflective surface. 28 Figure 2.6 presents SEM images of representative localized attack sites (Figure 2.6a,c,e) with a magnified view (Figure 2.6b,d,f) on the coupons of 6022-T4 after field exposure for 3, 5, 15 and 21 months. SEM examination revealed that all localized attack sites contained extensive IGC and intragranular corrosion in some of the grains adjacent to the

IGC fissures. Before the first winter (3 months shown in Figure 2.6a), intragranular attack in grains was generally less significant than the longer exposure time (Figure 2.6c-h). As indicated by the red arrows in Figure 2.6d and f, the intragranular attack was usually associated with areas of IGC. Geometric facets with an approximate side length of 1-2

µm can be observed in the cavity of a dissolved grain (marked by the red circle in Figure

2.6h). A similar phenomenon was referred to as crystallographic corrosion and usually reported after anodic polarization of Al alloys [124-126].

Figure 2.7 and Figure 2.8 show the exposed surfaces and SEM images of representative localized attack on the coupons of 6022-PB after field exposure. A similar corrosion morphology to 6022-T4 was observed as extensive IGC with intragranular corrosion similar to that observed in 6022-T4. Large intermetallic particles (IMPs) with a size of several microns can be observed on the SEM images of all tested alloys with higher brightness.

Although most of the IMPs were not necessarily related to localized corrosion, trenching around IMPs can be observed occasionally in the adjacent area of the IGC on

6022-T4 and PB. Examples of the large IMPs and trenching around the particle is indicated by the arrows in Figure 2.8f. Further analysis of the role of IMPs in localized corrosion is out of the scope of this chapter and will be included in Chapter 4.

29 2.4.2.2 6061-T4 and 6061-T6

Figure 2.9 presents coupon surfaces after field exposure. Similar to the 6022 alloys, 6061-T4 exhibited good resistance to uniform corrosion (Figure 2.9). SEM examination, shown in Figure 2.10, shows that 6061-T4 is generally more resistant to attack and IGC fissures were not as clearly identifiable as with 6022 in both tempers.

Additionally, large scale pitting was not observed. For 6061-T6, the primary corrosion morphology appears to be IGC wisps (small IGC segments), which can be observed by the arrows in Figure 2.10f and h. Similar corrosion morphology has been reported and associated with the tunneling phenomenon in Cu-containing 6xxx Al alloys [5, 7, 102].

According to the authors [102], the dendritic-shaped pitting attack is developed from expansion of IGC fissures due to re-deposition of Cu particles in the fissures.

Figure 2.11 and Figure 2.12 show the coupon surfaces and SEM of representative localized attack on the coupons of 6061-T6 after field exposure. SEM images in Figure

2.12 reveal that the predominant corrosion morphology on 6061-T6 is IGC with clear and extensive fissures. Similar to the 6022 alloys, grain dissolution can be observed in some of the grains surrounded by IGC fissures (Figure 2.12). After 21 months of exposure

(Figure 2.12 g-h), faceted cavities were observed and likely associated with grain fall out and/or intragranular corrosion. An example of such a severe cavity is shown in Figure

2.12h.

30 2.4.2.3 C26N-PB

Figures 2.13 and 2.14 show the coupon surface of C26N-PB after field exposure.

IGC with intragranular corrosion is seen as the dominant corrosion morphology. As with

6061-T6, IGC was so extensive in C26N-PB that it led to grain fall out and the formation of large cavities as seen in Figure 2.14h.

2.4.3 Cross-sectional Analysis

2.4.3.1 Corrosion Morphology on Cross-section

To further evaluate damage below the exposed surface, coupons for each alloy were sectioned and examined using optical microscopy. Figure 2.15 shows cross- sectional images typical of localized attack in the alloys investigated after 21 months of exposure. For 6022-T4 and PB shown in Figure 2.15a-b, both isolated IGC and pit- associated IGC (magnified view on the right of Figure 2.15 a and b) were observed. In these alloys, IGC was not extensive even after 21 months as, most isolated IGC fissures were shallow and remained unconnected as indicated by the boxed area in Figure 2.15a.

6061-T4 also exhibited limited susceptibility to IGC and grain dissolution with only wisps of IGC fissures observed (Figure 2.15c). Both 6061-T6 and C26N-PB (Figure

2.15d-e) displayed clear IGC that grew more extensively. This is particularly the case for

6061-T6 where the IGC was able to meet and connect deeply below the exposed surface

(Figure 2.15d). Slight attack on the grains along with the IGC fissures can also be observed for C26N (Figure2.15e).

31

2.4.3.2 Depth of Localized Corrosion

Figure 2.16 shows the average maximum depth of localized attack measured from optical images obtained by cross-sectioning. The average maximum depth for each alloy was calculated by averaging the total penetration depth of any attack site deeper than 10

µm. If there were more than 5 attack sites, only the 5 deepest were used. The error bars in

Figure 2.16 indicate one standard deviation away from the average. As shown in Figure

2.16, 6061-T6 exhibits the deepest attack across all time points, which reaches about 100

µm after 21 months of exposure. 6022-PB and C26N-PB performed near identically in terms of depth of attack over time and slightly less than 6061-T6, which the maximum corrosion depth at about 80 µm after 21 months of field exposure. 6022-T4 exhibited the same depth of attack of ~ 80 µm after 21 months of field exposure, but had statistically shallower depth than 6061-T6, C26N-PB, and 6022-PB for lesser exposure times. No cross-section of localized attack in 6022-T4 after 3 months was observed following the same procedure to the other samples due to low density of localized attack, so only the depths of longer exposure time are given in Figure 2.16. 6061-T4 shows the shallowest attack among these alloys with a depth of around 30 µm after 21 months.

The lines in Figure 2.16 are power-law fits to these data, 푑푒푝푡ℎ(푡) = 퐶푡푚.

Table 2.2 shows values of the fitting parameters and adjusted R-square of each fit. The power, m, of all the alloys except for 6022-T4 is found to be in the range of 0.3 to 0.5.

The power m of 6022-T4 is about 0.8, which is significantly higher than the other alloys.

32 2.5 Discussion

Regardless of different chemical compositions and heat treatments, all tested Al alloys were found to be susceptible to IGC during field exposure. The literature has attributed the susceptibility to IGC to the enrichment of alloying elements, e.g. Cu, Si,

Mg, along the grain boundaries [6, 8, 9, 87, 88, 101-108]. For 6XXX Al alloys with >0.1 wt% Cu, such as 6061 and C26N, susceptibility to IGC has historically been reported to increase with the formation of precipitates, such as β-Mg2Si, Q-phase (Al4Mg8Si7Cu2) and Cu nano-films, along grain boundaries [8, 9, 87, 88, 101-108]. Enrichment of Cu at grain boundaries leads to the formation of a solute-depleted zone (SDZ) in the adjacent region [8, 9, 102, 103, 109]. The resulting potential difference between these microstructural phases can cause a selective dissolution of the SDZ [8, 103, 104]. As a result, IGC can develop. For Al-Mg-Si alloys with low Cu content but low Mg/Si ratio, such as 6022, susceptibility to IGC can be attributed to the excess Si with respect to the

Mg/Si ratio that is required for the formation of Mg2Si phase (slightly below 2) [6, 8, 87,

88, 101]. Microsegregation of Si and an adjacent Si-depleted zone has been reported and correlated to the development of IGC due to microgalvanic effect between the noble Si at

GBs and adjacent Al matrix [6, 88].

Although all Al alloys studied showed susceptibility to IGC, intra-granular corrosion was also observed. The fact that intra-granular corrosion was found near IGC fissures suggests that IGC developed first during field exposure and likely triggered intragranular corrosion in selected grains. Dissolution of Al in IGC fissures causes enrichment of Al3+ ions and hydrolysis of water at the IGC front, leading to decreased pH

33 and increased concentration of chloride anions to keep the electrical neutrality. A previous study on AA2024-T351 suggests that low pH and the presence of Cl- ions at the corrosion front facilitates initiation of crystallographic dissolution of selected grains

[127]. The crystallographic nature of the intragranular corrosion observed in the SEM

(examples seen Figures 2.10 and 2.6) would support this hypothesis.

IMPs in Al-Mg-Si alloys are well known to be responsible for the initiation of pitting when immersed in chloride-based electrolytes [4, 128]. Although trenching around

IMPs can be observed occasionally on some samples as indicated by the arrows in Figure

2.8f, most of the IMPs remained unattacked after 2 years of on-road exposure. On the other hand, SEM examination shows that localized attack generally occurred without the presence of particle-associated pits on all the alloys investigated (Figures 2.6, 2.8, 2.10,

2.12, 2.14). Therefore, IMPs may not play an important role in the development of localized attack in field exposure of these alloys and the driver is largely grain boundary precipitates and associated solute depleted zones.

Although all of the alloys were found to be susceptible to IGC and intra-granular corrosion, after initiation of localized attack at grain boundaries, the depth of localized attack into the alloys is largely controlled by the propagation of IGC. This is supported by the total corrosion depth measured. The alloy only showing wisps of IGC, i.e. 6061-T4

(Figure 2.10), had the lowest depth of attack (Figure 2.16). With increasing susceptibility to IGC, the maximum depth of localized attack also increased with ordering from deepest to shallowest as: 6061-T6 > 6022-T4/PB, C26N-PB > 6061-T4 (Figure 2.16). This ordering is consistent with the literature explanations for the cause of IGC given that with aged/paint-baked, the depth of attack and degree of grain fallout due to IGC increased [6, 34 8, 9, 87, 88, 101-108]. Grain boundary precipitation and the development of solute depleted zones would be expected from these thermal treatments.

6061-T4 was also susceptible to IGC, but the IGC fissures were not as obvious.

This is likely because corrosion attack expanded to the adjacent region along with the fissures, which is attributed to the relatively low propagation rate of IGC and re- deposition of Cu that could also enhance intragranular corrosion. It has been proposed that Cu ions can be released from a Cu-containing film and Q-phase due to corroded GBs and re-deposit in the IGC fissures away from the front [5, 102, 103]. The re-deposited Cu can serve as a strong cathode and cause corrosion attack into the adjacent Al matrix [129,

130], leading to grain dissolution expanded IGC fissures.

6022-T4 and 6061-T4 were both in the naturally aged condition but exhibit different corrosion performance at the later exposure times likely due to the low Mg/Si ratio of 6022. Compared with 6061-T4 (Figure 2.15c), the excess Si in 6022 could increase the susceptibility to IGC [6, 8, 87, 88, 101]. Additionally, the lower Cu content of 0.05 wt% (vs. 0.28 wt.% in 6061-T4) may lead to less re-deposited Cu in IGC fissures and making IGC fissures more substantial (Figure 2.15a-b).

6022-PB and C26N-PB showed corrosion and IGC susceptibility in the intermediate range for all alloys examined in this study. These alloys are considered intermediate because the maximum depth of attack falls between 6061-T4 and 6061-T6

(Figure 2.16) and widespread IGC with intra-granular attack in selected grains adjacent to

IGC fissures (Figures 2.6, 2.8, 2.14).

6061-T6 has the highest susceptibility to corrosion and IGC. Corrosion morphology of 6061-T6 was also dominated by IGC, grain fallout, and grain dissolution 35 (Figure 2.12f and Figure 2.15d). Compared with the other alloys, a large cavity with a faceted bottom was observed after 21 months of exposure in field (Figure 2.12h).

Artificial aging is likely the cause of this increased susceptibility.

2.6 Conclusion

• Corrosion attack Al-Mg-Si alloys AA6022-T4/PB, AA6061-T4/T6, and C26N-PB

was highly localized on the exposed surface with the large majority of the surface

remaining unattacked after 2 years of exposure. This localized attack was largely

IGC based with some intragranular corrosion and initiated in a short time of field

exposure.

• The primary corrosion morphology was IGC with grain dissolution in spite of the

differing chemical compositions and temper conditions that would normally be

expected to alter susceptibility to IGC.

• The highest depth of localized attack after 2 years was obtained in 6061-T6, which

had the highest Cu content and artificial aging.

• Pitting caused by trenching around IMPs was seen in a limited number of cases but

did not dominate the corrosion morphology nor depth of attack observed after field

exposure.

• The overall corrosion morphology after field exposure was determined by a

synergetic process of IGC and intra-granular corrosion. Relatively pure IGC can be

obtained in the alloys with low Mg/Si ratio (6022-T4) or artificial aging (6061-T6

and C26N-PB). Intra-granular corrosion dominated 6061-T4, which has higher Cu

36 content but low susceptibility to IGC possibly due to the lack of artificial aging and excessive Si at grain boundaries.

37 Table 2.1: Alloy compositions in wt.%. Measured by ICP/OES. Si content was determined by a gravimetric method.

Mg Si Fe Cu Cr Zn Mn Ti Others Al 6022-T4 0.64 0.80 0.09 0.05 0.03 <0.01 0.06 0.02 <0.15 Bal. 6022-PB 0.64 0.80 0.09 0.05 0.03 <0.01 0.06 0.02 <0.15 Bal. 6061-T4 0.88 0.65 0.46 0.28 0.18 0.02 0.04 0.02 <0.15 Bal. 6061-T6 0.82 0.72 0.54 0.26 0.17 0.06 0.13 0.06 <0.15 Bal. C26N-PB 0.49 0.88 0.12 0.67 0.06 <0.01 0.05 0.03 <0.15 Bal.

Table 2.2: Fitting parameters and adjusted R-squares of power-law fits to the attack depth data shown in Figure 2.16.

log (C) (log (µm/hour)) m Adj. R-Square 6022-T4 -1.52 0.806 0.976 6022-PB 0.167 0.411 0.992 6061-T4 -0.471 0.499 0.771 6061-T6 0.303 0.418 0.903 C26N-PB 0.498 0.330 0.653

38

Figure 2.1: Location of the exposure rack at the bottom of a CABS bus and sample arrangement on the rack. Red arrow indicates the direction of observation for the image below.

39

Figure 2.2: Schematic of a sample coupon and the location of the cross-section for cross- sectional analysis. The dark grey section of the coupon was cut and mounted for cross- sectional analysis. The red cross-section was used. The arrow indicates direction of observation.

Figure 2.3: Example of total attack depth measurement.

40

Figure 2.4: Optical micrographs of the grain structure of the tested alloys. (a) 6022-T4; (b) 6022-PB; (c) 6061-T4; (d) 6061-T6; (e) C26N-PB. Longitudinal direction (L) represents the rolling direction. The short transverse (S) direction encompasses the full sheet thickness. The length of scale bars is 200 µm.

41

Figure 2.5: Exposed coupon surface of 6022-T4 after cleaning: a) 3 months; b) 5 months; c) 15 months; d) 21 months. (Each image shows a 2 in by 2 in area of the coupon.)

42

Figure 2.6: SEM and a magnified view of the boxed area of a representative site of attack on 6022-T4 after a-b)3 months, c-d) 5 months, e-f) 15 months, and g-h) 21 months of field exposure.

43

Figure 2.7: Exposed coupon surface of 6022-PB after cleaning: a) 3 months; b) 5 months; c) 15 months; d) 21 months. (Each image shows a 2 in by 2 in area of the coupon.)

44

Figure 2.8: SEM and a magnified view of the boxed area of a representative site of attack on 6022-PB after a-b)3 months, c-d) 5 months, e-f) 15 months, and g-h) 21months of field exposure.

45

Figure 2.9: Exposed coupon surface of 6061-T4 after cleaning: a) 3 months; b) 5 months; c) 15 months; d) 21 months. (Each image shows a 2 in by 2 in area of the coupon.)

46

Figure 2.10: SEM and a magnified view of the boxed area of a representative site of attack on 6061-T4 after a-b)3 months, c-d) 5 months, e-f) 15 months, and g-h) 21months of field exposure. The arrows are pointing to IGC fissures.

47

Figure 2.11: Exposed coupon surface of 6061-T6 after cleaning: a) 3 months; b) 5 months; c) 15 months; d) 21 months. (Each image shows a 2 in by 2 in area of the coupon.)

48

Figure 2.12: SEM and a magnified view of the boxed area of a representative site of attack on 6061-T6 after a-b)3 months, c-d) 5 months, e-f) 15 months, and g-h) 21months of field exposure.

49

Figure 2.13: Exposed coupon surface of C26N-PB after cleaning: a) 3 months; b) 5 months; c) 15 months; d) 21 months. (Each image shows a 2 in by 2 in area of the coupon.)

50

Figure 2.14: SEM and a magnified view of the boxed area of a representative site of attack on C26N-PB after a-b)3 months, c-d) 5 months, e-f) 15 months, and g-h) 21months of field exposure.

51

Figure 2.15: Optical microscopy images of cross-section of localized attack after 21 months of exposure for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N- PB. The inner images are a magnified view of the boxed areas.

52

Figure 2.16: Average maximum depth of localized attack measured on cross-sectional images in field exposure for 3, 5, 15, and 21 months. The maximum depth is calculated using the average of the five deepest sites. Error bars indicate one standard deviation from the average.

53 3. Corrosion Behavior of Al-Mg-Si Alloy in Accelerated Tests

3.1 Abstract

Three wrought Al-Mg-Si alloys with different aging conditions (6061-T4/T6,

6022-T4/Paintbaked, and C26N-Paintbaked) were exposed to laboratory accelerated corrosion tests in order to assess their ability to simulate the corrosion morphology found after service like exposure (i.e. that done in Ch.2). The corrosion test methodologies examined include an immersion test (ASTM G110) and salt spray tests (ASTM B117,

ASTM B368 (CASS), ASTM G85-A2 (MASTMAASIS), cyclic B117, and

GMW14872). After exposure in tests using a pH neutral solution, the predominant corrosion morphology was shallow pitting on the order of several microns for all alloys.

For 6061-T6 and C26N-PB, larger-scale pits with co-located intergranular corrosion

(IGC) were also observed in neutral pH testing, but with lower occurrence than shallow pitting. In contrast, all tested alloys except for 6061-T4 exhibited susceptibility to IGC in test solutions acidified to a pH ~3 (CASS and MASTMAASIS). Only larger-scale pits were observed on 6061-T4 in these two tests. In all, the observed corrosion morphologies after laboratory tests that use an acidified solution best match the results from the field exposure (results discussed in Chapter 2), suggesting that the pH of the test solutions has the stronger effect on corrosion morphology than addition of wet/dry cycles and cathodic accelerators. 54

3.2 Objectives

For the 6xxx Al alloys tested here, IGC with a combination of intragranular corrosion and grain fallout were observed after field exposure (results discussed in

Chapter 2). However, limited research has been conducted on bare Al-Mg-Si to correlate corrosion behavior in laboratory tests and field exposure without the added complexity of coatings. To this end, the following questions are addressed by this research:

• Do 6xxx Al alloys exhibit the same corrosion morphology in various accelerated

tests? If not, how does the corrosion morphology differ from test to test?

• Which environmental factor has the most significant effect on corrosion behavior

in laboratory accelerated corrosion tests? Corrosion behavior here is defined as:

o the evolution of corrosion morphology across all time points and alloys.

o the accumulation of depth of attack.

• Which laboratory corrosion test best simulates corrosion of 6xxx Al alloys in an

automotive environment when deicing salts are involved?

The following objectives are targeted to answer the above outlined questions.

• correlate environmental variables, pH, wet/dry cycle, cathodic accelerators, and

mixed salt solution, with specific corrosion morphology.

• quantify the severity of corrosion attack in the corrosion and compare with the

result from field exposure and determine the critical environmental factor that

enables laboratory corrosion test to simulate field behavior. 55

3.3 Experimental

3.3.1 Laboratory Corrosion Tests

3.3.1.1 Sample Preparation and Test Setup

Samples for laboratory corrosion testing were prepared by cutting sheets of 6022-

T4/PB, 6061-T4/T6, and C26N-Paintbaked (PB) into coupons with the T × L dimension of 2 in. × 4 in. (50.8 mm × 101.6 mm), respectively. Details regarding these alloys and definition of dimension are given in Section 2.3. The T × L surface was abraded for testing with silicon carbide papers to 1200 grit in water and cleaned with ethanol in an ultrasonic cleaner in order to remove scratches on the as-received surface.

The laboratory corrosion tests conducted in this work includes the immersion test

ASTM G110 [131], and five salt spray tests of ASTM B117 [132], ASTM B368 [133], a cyclically modified ASTM B117, ASTM G85-A2 [46], and GMW 14875 [134]. A detailed comparison of these tests and the pull-out times used is given in Table 3.1.

Details of each test follow.

For the ASTM G110 immersion test, the ground coupons were pre-cleaned in concentrated nitric acid (70%) for 1minute followed by a rinse using DI water. The test solution of ASTM G110 was prepared just prior to exposure by mixing 57 g NaCl and 50 mL H2O2 (30%) and diluted to 1.0 L with reagent water [131]. The test solution was then pre-heated and maintained at 303 °C during the exposure. The cleaned coupons were immersed in the solution for 24 hours, suspended from a stand to prevent contact with the

56 beaker wall. A solution volume of about 500 mL was used for each coupon to ensure exposure of at least 5 mL solution cm2 of immersed coupon. The experimental setup is shown in Figure 3.1.

All salt spray tests were conducted using a programmable Q-FOG® Model CCT-

600 corrosion test chamber. The back surface and edges of the coupons were sealed with vinyl electrical tape to avoid any edge effect. The coupons were placed on plastic racks and tilted to about 16° from the vertical direction with the ground L X T surface facing up. An example of the coupon placement during salt spray testing is shown in Figure 3.2.

The details of each accelerated test follow.

ASTM B117 is a continuous salt spray test that utilizes a solution of 5 wt.% NaCl prepared with deionized water at 35 °C [132]. For each alloy, four coupons were prepared in total and exposed to ASTM B117 for four pullout times, 2, 7, 14, and 30 days, respectively.

ASTM B368, also known as the Copper-Accelerated Acetic Acid-Salt Spray

(CASS) test, was conducted using a continuous spray of acidified 5 wt.% NaCl with

CuCl2 added as a cathodic accelerator. The solution of the CASS test was prepared by adding 0.25 g CuCl2·H2O to each liter of 5 wt.% NaCl solution, and adjusting the pH to

3.1 to 3.3 using glacial acetic acid [133]. A coupon of each alloy was pulled out after exposure for 8, 22, and 44 hours.

ASTM G85-A2 utilizes an acidified 5 wt.% NaCl solution by adding glacial acetic acid to achieve a pH of 2.8 - 3.0. Each 6-hour cycle in ASTM G85-A2 consists of a

¾-hour spray, 2-hour purging with ambient air, and 3¼-hour soak at high relative humidity (RH) above 95% [46]. This test was conducted following the dry bottom 57 procedure. Coupons were subjected to 4 cycles per day and pulled out after 2, 7, 14, and

30 days.

The cyclically modified ASTM B117 combined the 5 wt.% NaCl solution of

ASTM B117 with the 6-hour cycle, consisting of ¾-hour spray, 2-hour purging with ambient air, and 3¼-hour soak at high relative humidity (RH) above 95%, which was chosen to mimic ASTM G85-A2. The pullout times for the cyclic B117 test were 7, 14,

30, and 60 days. The cyclically modified ASTM B117 will be referred to in this work as

“cyclic B117”.

GMW14872 was conducted using a complex salt solution of 0.9 wt.% NaCl, 0.1 wt.% CaCl2, and 0.075 wt.% NaHCO3. A standard cycle of GMW14872 consists of 24- hour (1 day) exposure of an 8-hour ambient stage with salt spray, 8-hour soak at high RH, followed by 8-hour drying at elevated temperature [134]. The ambient stage with spray contains four 30-minute sprays separated by 1.5 hours of purging in between using ambient air. The testing done in this study modified the standard slightly by adding a 2- hour ambient stage at the end of a standard cycle due to equipment limitation. A schematic showing the GMW14872 cycle stages used in this study is shown in Figure

3.3. Solutions of reagent grade CaCl2 and NaHCO3 were prepared separately using deionized water and then added to a prepared NaCl solution to prevent the formation of insoluble precipitates. GMW14872 was not performed on 6022-PB nor 6061-T4 because these alloys were added into the study after the testing had already been conducted.

58 3.3.1.2 SEM and Cross-section Analysis

After exposure, coupons were rinsed with DI water, air dried, and cleaned following the corrosion product removal procedure in ASTM G1-C.1.2 [135]. SEM images of typical localized attack sites were obtained following the same process as described in Section 2.3. Cross-sectional micrographs of localized attack after exposure were obtained using the method described in Section 2.3. For attack sites deeper than 10

µm, the depth of each site was measured using FIJI software [123]. At least three cross- sections per sample with localized attacks were examined.

3.3.2 Methods to Quantify the Similarity in Corrosion Morphology between Field

Exposure and Laboratory Accelerated Corrosion Tests

3.3.2.1 Image Processing

The optical micrographs after cross-sectioning of 6022-T4, 6061-T4, and 6061-T6 after corrosion tests or field exposure were used for the quantitative analysis of corrosion morphology. The alloys were selected because they each exhibited corrosion morphologies different from each other in field exposure as discussed in Chapter 2.

Image processing using Matlab (version R2019a) was performed on cropped images such that individual/isolated sites were included in each image. An example of the cropping process of individual localized attack sites is shown in Figure 3.4. The white boxes show the regions cropped out of the larger full image. Since the corrosion attack on the tested alloys was highly localized in nature, individual sites of attack can be separated from

59 each other by the unattacked surface. The unattacked surface also served as a baseline separating the epoxy mount and corroded area. Each individual site of attack was then cropped separately from the original micrograph for analysis. The cropped zones with larger-scale localized attack (>10 µm in-depth) were processed to obtain the boundary and corroded area for individual attack sites. In order to compare quantification parameters of larger-scale attack with shallow pits (<10 µm in-depth), examples of the shallow pits were cropped from cross-sections of 6022-T4 in ASTM B117. Figure 3.5 shows an example of the resulting images after image processing. The cropped cross- sections were first binarized based on a global threshold of grayscale determined using

Otsu’s method [136], the result of which is shown for example in Figure 3.5b. Any isolated area with a size smaller than 4 pixels was eliminated from the binary image to reduce noise from image acquisition. The edge image of the corrosion feature is then generated from the binary image using the ‘Canny’ method [137], result of which is shown for example in Figure 3.5c. The Matlab code used for image processing is included in Appendix A.

3.3.2.2 Fractal Analysis

Fractal analysis was utilized in an attempt to quantify the complexity of individual corrosion sites with the goal of distinguishing different corrosion morphologies, such as pitting from intergranular corrosion (IGC) as IGC is inherently more complex in shape.

As a result, the fractal dimension (FD) of IGC should be higher than a hemispherical pit.

The type of fractal analysis chosen was the box-counting method [138, 139]. The box-

60 counting method describes the complexity of a shape by the change in the number of boxes required to cover the shape as the box size changes. The fractal dimension estimated using the box-counting method is defined as follows:

log 푁(휀) 퐹퐷 = − lim 휀→0 log 휀 where N is the number of boxes required to cover the complete boundary of the shape

(corrosion feature in the case) and ε is the box size. The box sizes, defined as the number of pixels along one side of the box, increased as the power of two, i.e., 1, 2, 4, … 2n pixels, where n is the smallest integer such that only 1 box is required to cover the whole image. Figure 3.6 displays an example of the resulting images with different box sizes up to 32 pixels. For each site of attack, the slope of a linear regression fit to the log-log plot of N and corresponding ε was the box-counting dimension of the attack site. An example of the N- ε plot with a FD of 1.366 is shown in Figure 3.7. The box sizes were converted to micrometers in order to compare the box-counting dimensions from cross-sectional images with different resolutions.

3.3.2.3 Length/Area Ratios

In addition to fractal analysis, two length/area ratios, L/A and L2/A, were used for quantification of corrosion morphology. The length/area ratios were chosen because not only the corrosion boundary complexity but also the amount of material removed due to corrosion is taken into account. The L/A ratio was obtained by dividing the boundary length, L, of an individual site of attack by the removed area, A, which is defined below.

Intuitively, IGC, which would have relatively long boundaries and small corroded area,

61 should have larger L/A ratio than a hemispherical pit. However, the L/A ratio depends on not only the morphology but also the severity of attack. For example, ideal hemispherical pits with different depths have the same corrosion morphology, but the L/A ratio of the cross-sections, which can be considered as a semicircle, is a function of pit depth.

Therefore, the L/A ratio may not be able to distinguish corrosion morphologies when the severity of attack is significantly different. The other ratio, L2/A, was calculated using the square of boundary length to get a dimensionless parameter that is independent on the size of a shape.

The removed area, A, for an individual attack site was approximately the total area of the pixels in the black region on the binary image (Figure 3.5b). The boundary length was estimated by calculating the length of the edge of the corrosion feature (for example the white line in Figure 3.5c). To minimize the effect of different magnifications of the original micrographs on the boundary length calculation, the edge image was resized to obtain a resolution of 0.5 µm/pixel using the bicubic interpolation method. An example of the processed image is shown in Figure 3.5d. The Matlab code is included in

Appendix A.

3.3.2.4 Morphology Classification using GoogLeNet Network

The machine learning method utilizing a convolutional neural network is expected to detect corrosion patterns induced by different corrosion mechanisms and quantify the correlation. GoogLeNet [1], an open-source convolutional neural network (CNN), was used to quantify the similarity between field and laboratory corrosion results. This model

62 was pre-trained with a large image database, ImageNet [140], for general object recognition and re-trained in this work for corrosion morphology classification through transfer learning. An example of the acquisition process for input images is shown in

Figure 3.8. As required by the network, all the input images need to have a size of 224- by-224 pixels. Hence, optical micrographs of cross-sections after accelerated corrosion testing and field exposure were first cropped to meet the requirement regardless of exposure time and scale. The portion of cropped images not containing any corrosion or exposed surfaces were not used for re-training as they did not contain any information on corrosion behavior.

GoogLeNet, like the other CNNs, essentially consists of an input layer, a series of convolution layers, and output layers [1]. Figure 3.9 shows schematics of a simplified structure of the original GoogLeNet model and the transfer learning process. The layers of pre-trained model (Figure 3.9a) were connected with trainable parameters optimized for object recognition. In order to adjust the pre-trained model for corrosion morphology classification, the final layers of the original model, including the fully connected layer and classification layer, were initiated and replaced with laboratory corrosion tests as outputs. The early layers, which contains the weighted parameters for pattern detection, such as lines and corners, were frozen during retraining (Figure 3.9b). 70% of the cropped images from corrosion tests were randomly selected and labeled with corresponding test for retraining, while 30% of the cropped images were used to verify the fine-tuned model. To expand the training database, the input images were randomly flipped, rotated, and magnified up to 2 times of the original size. The validation accuracy reached around 80% for all the alloys. 63 The cross-sectional images after field exposure were cropped following the same procedure. The re-trained model was then used to classify the field exposure images into a laboratory test (Figure 3.9c) as a means of testing how closely aligned the corrosion morphology after field exposure matched that after laboratory accelerated testing. The field images were processed in the same way as the training images from the laboratory accelerated testing. For each of the field images being classified, the probability of that image matching with each laboratory test was generated using the softmax function that normalizes outputs of a machine learning algorithm into the (0,1) interval with a sum of 1 for all components. The probability, referred to as confidence of correlation, can then indicate the relative similarity of corrosion morphology between field exposure and laboratory tests. As such, for the purposes of this study, the confidence of correlation is used as a measure of fitness for how well the field exposure corrosion morphology is matched to an accelerated corrosion test. This can then be used to state how accurately an accelerated test can predict field exposure corrosion morphology.

3.4 Results

3.4.1 Corrosion Morphology in Laboratory Corrosion Tests

Four distinguishing corrosion morphologies were observed in laboratory corrosion tests, including shallow pitting, larger-scale pitting, isolated IGC, and pit- associated IGC. Figure 3.10 presents examples from cross-sections of these four categories. As shown in Figure 3.10a, the shallow pitting can be identified by a near- hemispherical shape and depth generally below 10 µm. Figure 3.10b-c show larger-scale 64 pits distinguished from the shallow pits because of their large depth (> 10 µm) and irregular shape. The larger-scale pits could exhibit either relatively smooth (Figure 3.10b) or rough (Figure 3.10c) boundaries. As shown in Figure 3.10d, isolated IGC can be observed to occur separately from other corrosion morphologies. Larger-scale pits and pit-associated IGC were also seen as shown in Figure 3.10e-f. This corrosion morphology can be dominated by either pitting with wisps of IGC fissures, as seen in Figure 3.10e, or extensive IGC, as seen in Figure 3.10f. The bottom boundary of the cavity associated with extensive IGC is likely composed of small line segments (see the yellow polyline in

Figure 3.10f), which is a strong indication in favor of detachment of whole grains after

IGC as opposed to actual pit-associated IGC. However, explicit line segments were not always observed at the bottom owing to grain dissolution, so it was not clear whether these cavities were caused by grain fallout or complete dissolution of grains. Therefore, the corrosion morphology shown in Figure 3.10f is referred to as pit-associated IGC in this work.

Localized corrosion in laboratory tests usually involved multiple corrosion morphologies as summarized in Table 3.2. Colored boxes indicate alloys with similar corrosion behavior. It can be seen from the table that the corrosion morphology, especially that for 6022-T4/PB, is highly dependent on the pH of the solutions used in accelerated testing. During exposure in neutral NaCl solutions, larger-scale pitting, pit- associated IGC, and/or isolated IGC was only noted on 6061-T4/T6 and C26N-PB, while, after the tests using acidified solution, all of the alloys underwent larger-scale pitting.

Additionally, shallow pitting was absent from tests utilizing acidified solution. The

65 specific results from each laboratory accelerated corrosion test will be outlined in the following sections.

3.4.1.1 ASTM G110

Figure 3.11 shows the coupon surface of all alloys tested after 24 hours of exposure to

ASTM G110. No localized attack was observed by visual examination on 6022 in the T4 or PB condition (Figure 3.11a-b). For 6061-T4 and T6 and C26N-PB, light grey spots can be seen on the coupons after 24 hours of ASTM G110 testing (Figure 3.11c-e). Figure

3.12 presents representative cross-sectional micrographs for all alloys tested. For 6022 in both the T4 and PB condition, cross-sectional analysis confirms that 24 hours in ASTM

G110 was insufficient to generate corrosion damage (Figure 3.12a-b). No further testing was conducted on this alloy in ASTM G110 solutions. For 6061-T4, only shallow pittings were observed (Figure 3.12c). Both 6061-T6 and C26N-PB exhibited susceptibility to shallow pitting, larger-scale pitting, isolated IGC, and pit-associated IGC (Figure 3.12d- g).

3.4.1.2 ASTM B117

The coupon surface and a corresponding magnified view for each alloy after 30 days of exposure in ASTM B117 are shown in Figure 3.13. For 6022 in the T4 and PB conditions, Figure 3.13a-b show evidence of pitting as indicated by the round-shaped dark spots on the surface. For 6061 in the T4 and T6 conditions and C26N-PB, Figure

3.13c-e show larger-scale attack sites with irregular outlines in addition to smaller round 66 spots. Figure 3.14 shows representative optical micrographs of cross-sections of all alloys after 2, 7, 14, and 30 days of ASTM B117 exposure. For 6022-T4 and PB, only shallow pitting was observed across all exposure times. For 6061-T4, shallow and larger-scale pitting with no evidence of IGC developed after all exposure times. It is important to note that pits on the order of 20 microns were found on 6061-T4 in as little as 2 days, which implies that larger scale pitting started and grew in as little as 2 days. For 6061-T6 and

C26N-PB, Figure 3.14 shows that shallow pitting, larger-scale pitting, and pit-associated

IGC had developed in as little as 2 days of exposure in ASTM B117 and persisted for all exposure times examined.

3.4.1.3 Cyclic B117

Figure 3.15 shows the exposed coupon surface of all alloys tested after 60 days of exposure in cyclic B117. Evidence of pitting corrosion was found on the surfaces of all alloys after 7, 14, 30, and 60 days of exposure. Figure 3.16 shows the cross-sectional micrographs after exposure in cyclic B117 for 60 days. The predominant corrosion morphology on all alloys was shallow pitting even after the longest exposure time of 60 days. As such, only micrographs after 60 days of exposure are shown here for all the alloys except for 6061-T6. For 6061-T6, in addition to shallow pitting, IGC was occasionally observed, as shown at 60 days in Figure 3.16d and after 7 days in Figure

3.16f. As a reminder, the exposure times for the cyclic B117 test were 7, 14, 30, and 60 days. For the same exposure time of 30 days, the continuous B117 caused larger-scale

67 pitting with IGC besides shallow pitting, which was the only corrosion observed after cyclic B117.

3.4.1.4 GMW 14872

Figure 3.17 shows representative optical images of the coupon surface after exposure of GMW 14872 for 54 cycles. For all three alloys tested in GMW 14872, 6022-

T4, 6061-T6, and C26N-PB, the exposed surfaces show evidence of substantial corrosion. Upon cross sectioning, shallow pitting was found in all alloys at all exposure times, as shown in Figure 3.18. For 6022-T4, shallow pitting appears to grow over time into hemispherical larger pitting on the order of 20 μm deep by 54 cycles. In addition to shallow pits, isolated IGC and extensive IGC were observed to dominate the corrosion morphology on 6061-T6 and C26N-PB (Figure 3.18). IGC multiple grains deep was seen on 6061-T6 in as little as 2 cycles of exposure to GMW 14872. Small fissures stemming from small pits were seen on C26N-PB in as little as 2 cycles with larger scale isolated

IGC was seen in 7 to 14 cycles.

3.4.1.5 CASS

Figure 3.19 shows that after exposure to CASS for 44 hours, sites of localized attack and corrosion product can be visually identified on the coupon surface. Figure 3.20 shows the corrosion morphology in cross-sections after 8, 22, and 44 hours of exposure to

CASS. It can be seen that larger-scale pitting and wisps of pit-associated IGC developed in 6022-T4 and PB and C26N-PB after 8 hours. Wisps of isolated IGC were also found 68 emanating from the surface on these three alloys as indicated by arrows in Figure 3.20. In

6061-T4, larger-scale pitting with evidence of grain dissolution was observed starting from 8 hours of exposure in CASS. For 6061-T6, pit-associated IGC was the predominant corrosion morphology with extensive attack present in as little as 8 hours.

IGC was most extensive in 6061-T6 after all CASS exposure times and likely led to pitting due to grain fallout. Only wisps of IGC were observed in all other alloys. In general, the corrosion morphology of all the alloys did not change with time of exposure but the depth of attack increased with time. Analysis on the depth of attack will be given in Section 3.4.2.

3.4.1.6 ASTM G85-A2

Corrosion morphology after exposure in ASTM G85-A2 can be seen in Figures

3.21 and 3.22. Figure 3.21 shows evidence of localized attack after 30 days of ASTM

G85-A2 exposure. Cross-sections showing the representative corrosion morphology after

2, 7, 14, and 30 days of exposure are shown in Figure 3.22. Larger-scale pitting is seen for all alloys tested after 30 days of exposure. For 6022 in both the T4 and PB conditions, small wisps of pit-associated IGC were also seen at the longer exposure times emanating from both surfaces and larger scale pits. Larger-scale pitting was the primary corrosion morphology observed on 6061-T4 across all time points. For the later times of 14 and 30 days, there were small wisps like fissures that may be due to small quantities of IGC or maybe just an edge of a meandering pit. Testing out to longer times and/or more intensive

3D examination of pit shape would be required to determine what causes small features.

69 6061-T6 exhibited extensive pit associated IGC in as little as 2 days. IGC was most extensive in 6061-T6 and likely caused cavities associated with it by grain fallout, in 2 and 7 days of exposure. C26N-PB showed larger-scale pitting attack after as little as 2 days and small amounts of pit-associated IGC with wisps of IGC emanating from surfaces and pits.

3.4.2 Corrosion Attack Depth

Figure 3.23 shows the average maximum corrosion depth of attack for sites deeper than 10 µm as measured in cross-section as a function of exposure time for all alloys and test environments. The trend lines shown in Figure 3.23 show a power law fit.

Any data set not included in Figure 3.23 were excluded due to attack only being below 10

µm. The data not included are as follows. For 6022-T4 and PB, ASTM G110, ASTM

B117, cyclic B117 and GMW14872 at all exposure times. For 6061-T4, ASTM G110, cyclic B117, and GMW14872 at all exposure times. For 6061-T6 and C26N-PB, cyclic

B117 and GMW14872 at all exposure times.

As shown in Figure 3.23, average maximum depth generally increased with exposure time for all alloys and test conditions. In corrosion studies, it is conventional to assume that corrosion grows with time following a power law function [141]. As such, the average maximum depth of attack was fitted with a power law function, depth = Ctm, where depth is the average maximum depth of attack, C is a fitting coefficient, and m is a exponent. The fitting lines are shown in Figure 3.23 and the fitting parameters and adjusted R-squares for the fits were given in Table 3.3. As discussed in Chapter 2, m for

70 field exposure generally varied between 0.3 and 0.5 and C was below 3 µm/h for all the alloys except 6022-T4, which had a higher value of m probably due to the lack of data.

Compared with field results, CASS led to a significantly higher C, above 23 µm/h for each alloy, but a slightly lower exponent, m. ASTM G85-A2 generally exhibits both C and m slightly higher than those from field exposure. No general tendency was observed for ASTM B117, but both the fitting parameters were close to those in field exposure.

3.4.3 Quantified Assessment of Correlation of Corrosion Morphology

Fractal analysis, length/area ratios, and CNN-GoogleNet are used to attempt to quantify the similarity in corrosion morphology between laboratory corrosion tests and field exposure. Considering the complex corrosion morphologies that evolved after exposure, quantified methods are better suited to estimating the ability of accelerated laboratory corrosion tests to simulate the in-service/field corrosion behavior. As shown in

Table 3.2, the alloys investigated in this study can generally be classified into 3 categories depending on the predominant corrosion morphology. The first is shown in the green box in Table 3.2 and contains the 6022 alloys of both tempers, where acidic testing showed IGC, pit-associated IGC, and larger-scale pitting while the pH neutral tests showed no corrosion or shallow pitting. A second category, shown in red in Table 3.2, contains 6061-T6 and C26N-PN where IGC in some form was found in all accelerated laboratory testing. The final category belongs to 6061-T4 and is shown in orange in Table

3.2 where no IGC was found in any of the laboratory accelerated tests. Based on similarities in each category, only one alloy from each of the categories was used for

71 quantitative analysis. The alloys utilized were 6022-T4 (green box), 6061-T4 (orange box), and 6061-T6 (red box). Figures 3.24 – 3.26 show the quantitative analysis parameters (i.e. FD in Figure 3.24, L/A in Figure 3.25, or L2/A in Figure 3.26) as a function of time in the left column and cumulative probability (CP) as a function of this quantitative analysis parameter in the right column. For Figures 3.24-3.26 b, d, and f (the right columns), CP as a function of fractal dimension (FD), L/A, and L2/A are shown for only one exposure time per test method. The exposure time chosen was that with the similar average maximum depth of attack to 21 months of field exposure. The exposure time for each test is indicated by the arrows in Figure 3.24-3.26a, c, and e. For example, for 6022-T4, 21 months of field exposure was approximately equivalent to about 8 hours of CASS and 7 days of ASTM G85-A2 (Figure 3.23a). As such, the CP plots in Figure

3.23b are for 21 months field exposure, 8 hours CASS, and 7 days ASTM G85-A2. The following sections discuss the results for each quantitative analysis parameter.

3.4.3.1 Fractal Analysis

Figure 3.24 shows the FD estimated using the box-counting method in corrosion tests and field exposure for the alloys under investigation in this method. Note that only shallow pits can be observed on 6022-T4 in the corrosion tests using pH-neutral solutions. As such, examples of the shallow pits after only 30 days of exposure in only

ASTM B117 only were processed and used to represent all pH-neutral solutions (i.e. cyclic B117 and G110) for this alloy/temper. The plots on the left of Figure 3.24 display the variation of average FD during exposure in different conditions. The error bars

72 indicate the maximum and minimum value of FD obtained at the corresponding time of exposure. Note that the average value may not be the optimum way to compare among different test conditions due to the large range of FD for a specific exposure time and different severity of the localized attack. Therefore corresponding CP are plotted on the right side of Figure 3.24. From FD as a function of time in Figure 3.24a,c, and d, it can be seen that the average FD was not significantly altered by exposure time in CASS and

G85 laboratory corrosion tests and field exposure for all the alloys. The average FD of the localized attack in 6022-T4 was evaluated to be about 1.2 in both the acidified tests

(CASS and ASTM G85-A2) as well as the field exposure. The shallow pits on 6022-T4 after 30 days of ASTM B117 exhibited slightly lower FD of 1.1 (Figure 3.24a). For 6061-

T4 and T6, the average FD in field varied between 1.35 to 1.40, generally higher than results from laboratory corrosion tests. CP of FD given in Figure 3.24b,d ,and f show the

FD of all sites of attack at the exposure time of interest for each alloy. It can be seen that the distributions of FD from CASS are generally in agreement with those from field exposure for all the three alloys. These results along with the consideration that ASTM

G110 was too mild to cause severe attacks on 6061-T4, suggests that CASS, in general, led to the best match of FD to field exposure with similar depth of attack.

3.4.3.2 L/A

Figure 3.25 shows the L/A ratios of cross-sections of localized attacks on 6022-

T4 and 6061-T4 and T6 after laboratory and field exposure. It can be seen that the average L/A ratios were not altered by increasing time of exposure in the field exposures,

73 but did slightly decrease as a function of time in CASS and ASTM G85-A2. For 6022-T4 and 6061-T6, the average L/A ratios in field were kept at about 2, which is higher than that for the laboratory corrosion tests. For 6061-T4, the average L/A ratio was much lower than the other alloys below 1.0 in all test environments, but the field exposure L/A was still slightly higher than that for the laboratory corrosion tests. Unlike with fractal dimension analysis, the L/A ratio for the shallow pits on 6022-T4 after ASTM B117 was similar to that in CASS and ASTM G85-A2 (Figure 3.25a-b). CP of L/A ratio in Figure

3.25 b,d, and f, show that the L/A ratios from laboratory corrosion tests are close to each other and significantly lower than that from field exposure. Therefore, the L/A ratio may not be suitable to distinguish laboratory corrosion tests that simulate corrosion morphology in field exposure from other tests.

3.4.3.3 L2/A

Figure 3.26 shows L2/A analysis. Unlike L/A, the average L2/A ratios in laboratory corrosion tests were not altered by exposure time for all the alloys. During field exposure, the maximum values of L2/A of 6061-T6 increased dramatically with time as well as the average values (Figure 3.26e). The CP profiles for all alloys in CASS and

ASTM G85-A2 almost overlapped each other (Figure 3.26b,d, and f). The L2/A ratios of field exposure were higher than those of CASS and ASTM G85-A2. ASTM B117 had

L2/A ratios furthest from the field exposure. A discontinuity can be observed on the CP curve for the L2/A ratio in ASTM B117, where the value increases suddenly from 20 to

100 at the CP of about 0.8. The data points below 20 L2/A were attributed to larger scale

74 pits (depth > 10 µm) that with smooth boundaries similar to those of the hemispherical shallow pits. The results are generally consistent with fractal analysis, which is Field ≈

ASTM G110 > CASS > ASTM G85-A2 > ASTM B117.

3.4.3.4 Morphology Classification Based on Machine Learning

Figure 3.27-3.29 show the results from image recognition using GoogLeNet network. For each cross-sectional image being classified, a confidence of correlation

(probability) to each accelerated test was generated and normalized with softmax function. Therefore, the sum of the output confidences is 1 for an individual sample image. A higher confidence to an accelerated test means that the test shares more features with the training set from field exposure. In all plots, the x-axis indicates the image analyzed and the y-axis is the confidence of correlation value for that image and the given test environment. For each image (i.e. sample number), the sum of all test confidence of correlation values will be 1. Therefore, the test with the highest confidence of correlation for that individual sample number is the test sharing the most features. The pretrained algorithm was fine-tuned for 6022-T4 and 6061-T6 with 5 outputs, CASS,

ASTM G85-A2, ASTM B117, ASTM G110, and GMW14872; while for 6061-T4, 4 outputs were used (GMW14872 was excluded).

Figure 3.27 shows the result for 6022-T4. About 280 cross-sectional samples were classified, and 70% samples showed a high confidence of correlation with ASTM

G85-A2. Since the confidence was normalized to 1, a confidence over 0.5 to a certain test ensure a better match of corrosion morphology than to the other tests based on the

75 machine learning method. For the remaining samples that did not correlated with ASTM

G85-A2, the highest confidence was obtained with either CASS (2 %) or ASTM B117

(28 %). Figure 3.28 shows the confidence of correlation to corrosion tests for 6061-T4.

About 60 % of the 144 samples exhibited a correlation with over 0.5 confidence with

CASS, while only about 7 % matched with ASTM G85-A2. It can also be seen that about

30 % of the samples have relatively low confidences (below 0.5) to all the output tests. A similar phenomenon can be observed on 6061-T6 as shown in Figure 3.29. Samples of

6061-T6 in field exposure were most highly confidently classified as CASS (38 %) and

ASTM G85-A2 (25 %) with over 0.5 confidence of correlation. In addition, the confidence of correlation to GMW14872 was close to zero for both 6022-T4 and 6061-

T6.

3.5 Discussion

3.5.1 Ability of Laboratory Tests to Match Field Exposure

3.5.1.1 Visual Examination

The laboratory accelerated tests using neutral NaCl solutions (i.e. not acidified with acids), which were ASTM B117, ASTM G110, cyclic B117 and GMW 14872, were not able to generate the corrosion morphology observed in field exposure by visual examination. The observed corrosion morphologies in laboratory corrosion tests and field exposure are included in Table 3.2. For 6022-T4 and PB and 6061-T4, only shallow pits were observed in the laboratory corrosion tests even at the longest exposure times

76 conducted in this study; while, as shown in Table 3.2, little shallow pitting was observed after on-road exposure (Chapter 2). For 6061-T6 and C26N-PB, only ASTM B117 and

ASTM G110 among these neutral NaCl tests were able to generate the pits and pit- associated IGC similar to what was observed in field exposure.

Generally, ASTM G85-A2 and CASS outperformed the other tests in terms of simulating the corrosion morphology in field exposure of all alloys. For all alloys except for 6061-T4, pitting and pit-associated IGC can observed in field exposure as well as

CASS and ASTM G85-A2 (Table 3.2). For 6061-T4, pits with irregular topology were observed after field exposure, CASS, and ASTM G85-A2 testing. The IGC fissures in field were only wisps and insignificant comparing with the scale of the pits, whereas in

CASS and ASTM G85-A2 no clear IGC fissure was observed, but there appear to possibly be small wisps. Therefore, these two tests were generally able to match the corrosion morphology in field exposure, even if the magnitude of IGC varied depending on test environment.

3.5.1.2 Fractal Dimension, L/A, and L2/A

FD was utilized to characterize and compare singular corrosion sites after exposure and quantitatively evaluate the ability of laboratory accelerated tests to simulate on-road exposure. Fractal dimension analysis was chosen for its ability to quantify the complexity of the corroded boundary; and as such, it may be able to distinguish between

IGC, which would have a very complex corroded boundary, and pitting, which would be

77 relatively smooth in comparison with IGC in an equi-axed microstructure. Cross-sections with more complex edges have a higher FD.

For 6022-T4, FDs of the cross-sections suggested that CASS and ASTM G85-A2 simulate the complexity of the corrosion front in field exposure equally well (Figure

3.24a-b). For 6061-T4, FDs of all plotted laboratory tests were slightly lower than field exposure; but among those tests, the CASS test was closest to field exposure and was the only test that generated FDs above 1.4 like those observed in the field exposure (Figure

3.24d). The increased FD for field exposure tests may be due to the IGC fissures present in field testing that were not clearly observed in laboratory tests for this alloy, which would be predicted to raise FD by making the corrosion feature topology more complex.

The data for the FD examination is most abundant for 6061-T6. This analysis again shows that, CASS produces a closer match with field exposure than most tests. The exception is that ASTM G110 had a very similar cumulative probability distribution to the field, but the inability to produce corrosion in 6022 at either temper and only shallow pitting in 6061-T4 rules out ASTM G110 as a good match for the field. It also did not allow for the inclusion of ASTM G110 in this type of examination for all alloys.

Although peroxide is a strong oxidizing agent, the designed exposure time described in

ASTM G110 standard of 24 hours for more corrosion resistant Al alloys like 6xxx alloys was relatively short and peroxide may degrade with time. Therefore longer exposure times or replenishment of peroxide for ASTM G110 may be required to understand if

ASTM G110 would predict a sufficient match for 6xxx alloys. Overall, the FD analysis would suggest that CASS is the laboratory accelerated test that best predicts field exposure and requires the shortest time. 78 As mentioned previously, multiple corrosion mechanisms can be involved in localized corrosion of Al-Mg-Si alloys. Corrosion boundary length divided by the corroded area, L/A, was an intuitive way to characterize corrosion morphology because as IGC occurs the corroded boundary length will be much larger than the removed area leading to a higher L/A value. Should grain fallout occur, a higher corroded boundary length than conventional pitting would still exist, but the increased A compared to IGC without grain fallout would lead to an intermediate value of L/A. While the L/A ratio is useful, it may not be able to distinguish different corrosion types for this reason. As such, the L2/A ratio was also examined to reduce the effect of the corroded area.

For 6022-T4, the L2/A ratio of CASS and ASTM G85-A2 indicates a similar level of IGC to the field exposure (Figure 3.26b). For 6061-T4, pitting corrosion in CASS,

ASTM B117, and ASTM G85-A2, led to similar length/area ratios, which are slightly lower than those in field exposure due to IGC wisps (Figure 3.25d and Figure 3.26d). For

6061-T6, localized attacks in ASTM G110, CASS, and ASTM G85-A2 best matched the field exposure with regard to the L2/A ratio (Figure 3.26f). All in all, the CASS test best matches the result from field exposure for all alloys in general. Results from the L2/A ratio and fractal analysis are consistent in ranking of similarity between laboratory corrosion testing and on-road field testing and with visual examination as shown in Table

3.2, but the L2/A ratio exhibits a larger difference between different morphologies. For example, in ASTM B117, no larger-scale pitting or IGC was observed on 6022-T4. The data points of 6022-T4 given for ASTM B117 in Figure 3.24 a-b, Figure 3.25 a-b, and

Figure 3.26 a-b are only plotted here to compare the values of the parameters from shallow pitting with those from larger-scale attack. It can be seen that only the L2/A ratio 79 of shallow pitting in ASTM B117 can be easily distinguished from the L2/A ratio in the other tests, which caused larger-scale attack.

The FD and L2/A of individual sites of attack were plotted together for 6022-T4

(Figure 3.30), 6061-T4 (Figure 3.31), and 6061-T6 (Figure 3.32) with corresponding corrosion morphologies observed after laboratory and field exposure. It can be seen that grain dissolution and/or IGC generally led to higher FD and L2/A, while pits with smooth boundaries exhibited lower FD and L2/A. Different corrosion morphologies may not be distinguished by a single parameter. For example, with a FD of about 1.25, corrosion morphology varied from a shallow pit to pure IGC by increasing L2/A from 10 to 1000

(Figure 3.30). Therefore, corrosion morphology can be better described using both parameters.

3.5.1.3 Morphology Classification using ML

The fast development of deep machine learning also provides a potential solution for corrosion morphology recognition. Unlike fractal analysis, which only makes use of the corrosion boundaries, deep learning algorithms can detect low level features, such as edges and corners, and use the features for classification of new inputs [142]. CNNs can potentially learn the subtle features of corrosion morphology and quantify them for comparison [142]. Attempts have been made to apply machine learning methods at a deep neural learning level for cracked and uncracked area segmentation, and corrosion area segmentation from background on natural color images [143-146]. In these studies, input images labeled with crack/uncrack or corroded/uncorroded were used to train

80 CNNs and then the accuracy of CNNs were calculated using another set of input images.

Overall, the CNNs in the literature outperformed traditional computer vision filtering techniques in accuracy of feature detection [142]. Different corrosion morphologies have different cross-sectional features that may be used by CNN for classification of corrosion morphology.

The results from image classification using GoogLeNet are generally in good agreement with visual observation and fractal analysis and L2/A ratio. It can be seen that either ASTM G85-A2 or CASS has the highest confidence of correlation with the corrosion morphology after field exposure for all the alloys investigated, while the confidence of correlation was always low for ASTM B117 and GMW 14872 (Figure

3.27-3.29). Therefore, ML classification could also facilitate an assessment of the viability of using optical micrographs of cross-sections to predict the proper laboratory accelerated test for future predictions of performance.

Note that, for 6061-T6, both the fractal analysis and the L2/A ratio of the localized attacks in ASTM G110 match those in field exposure slightly better than CASS and

ASTM G85-A2 (Figure 3.24f and Figure 3.26f). However, the result from CNN classification suggests that corrosion morphology in field test correlated better with

CASS and ASTM G85-A2 than ASTM G110 (Figure 3.29). Based on results from visual examination, CASS, ASTM G85-A2 and ASTM G110 all caused larger-scale pitting and pit-associated IGC on 6061-T6, and the corrosion morphologies observed were too similar to be distinguished from each other. The reason for the difference is not clear because deep leaning algorithms, like GoogLeNet, classify images based on subtle features (low-level features), and they cannot provide enough information on what 81 corrosion-related features are used for classification. One possible explanation is that the

ML method is not sensitive to the corroded area. As mentioned before, deep learning algorithms can detect low level features of a shape, such as edges, and use the features for objective recognition [142]. However, the corroded region on the cross-sectional images does not contain any low-level features, so the deep learning model was not likely taking corroded areas into account. In contrast, the L2/A ratio used the corroded in the calculation.

In conclusion, all the approaches mentioned above provide potential ways to quantitatively compare different corrosion morphologies using statistical methods. It is found that the L2/A ratio can better distinguish different corrosion morphologies than FD and L/A ratio. The ML method could detect low level features and generate confidence of correlation for each input image, but the accuracy was limited by the size of the training set. In general, all the approaches achieved the same result.

3.5.2 Corrosion Morphology and Test Condition

3.5.2.1 Shallow Pitting and Isolated IGC in Neutral pH Solutions

Shallow pitting was the most common corrosion morphology observed in the tests using solutions with neutral pH (Table 3.2). As shown by SEM and EDS analysis in

Chapter 2, all alloys contain coarse Fe-rich intermetallic particles (IMPs). Birbilis et al. have shown using micro-electrochemical measurements that Fe-rich compounds in Al alloys are cathodic to the Al matrix [93, 94]. Due to the potential difference between these secondary phases and Al matrix, cathodic reactions are supported on these 82 secondary phase particles, leading to a change of chemistry highly local to the particle and subsequent corrosion attack due to a microgalvanic effect between the particles and

Al matrix [4, 5, 7, 11]. With further exposure, a trench can grow around, including below the particle leaving a shallow hemispherical pit after detachment of the particle [126].

Examples of the shallow pits with a hemispherical shape can be seen on 6022-T4 after exposure to the tests using solutions with neutral pH (Figure 3.14 and Figure 3.16). In addition, studies on β-Mg2Si particles reveal that the β phase is initially more anodic than the Al matrix, but becomes cathodically active like the Fe-rich particles after dealloying of Mg [11, 147]. The critical depth for a stable pit to grow on 2024-T3 was found to be approximately 13 µm, which was estimated using the anodic current spikes at OCP assuming a hemispherical pits [148]. This is very similar to the depth of the shallow pits in this study that did not transform to a larger-scale pits.

Shallow pitting was not observed in the field exposure or in CASS and ASTM

G85-A2, which use acidified solutions (Figure 3.20 and Figure 3.22). In a neutral electrolyte, pits tend to nucleate at the particle interface with the Al matrix because of alkaline dissolution owing to enhanced cathodic reaction on noble particles [149]. It is not clear why the acidified solution hinders the development of particle-induced pitting, but the lower pH could decrease alkaline dissolution near cathodic particles by neutralizing the local chemistry and then, suppresses the formation of trenching around the particles. This is supported by an occluded anode study conducted by Leclere et al. on a 2024-T3-like alloy [150]. It was found that the net cathodic current supplied by the cathode increased when a pH7 buffer was added to NaCl solution [150]. The authors suggested that this was because alkaline dissolution on the cathode was suppressed by the 83 buffer, and, therefore, cathodic current would not be “wasted” on alkaline dissolution and more net cathodic current was measured between the cathode and occluded anode [150].

Isolated IGC, which was associated with shallow pits but not larger-scale pits, was observed in 6061-T6 and C26N-PB after exposure to ASTM G110 (Figure 3.12e and

3.12g), ASTM B117 (Figure 3.33), cyclic B117 (Figure 3.16d and 3.16f) and GMW

14872 (Figure 3.18). Based on a SEM morphological analysis and TEM/STEM characterization, Shi et.al [6] proposed a mechanism bridging shallow pitting and IGC.

The authors [6] suggested that shallow pitting that forms at grain boundaries facilitated initiation of isolated IGC. On the other hand, if pitting occurs in grains instead of grain boundaries, the pit propagates within the grains leading to grain dissolution [6].

3.5.2.2 Larger-scale Pitting in Neutral pH Solutions

For 6061-T4 in ASTM B117, larger-scale pits with an irregular shape and smooth boundaries were observed in addition to shallow pits (Figure 3.14). The larger-scale pits are likely attributed to clusters of IMPs [113, 117]. As stated above, previous studies on

2XXX Al alloys reported that trenching around an individual cathodic intermetallic particle alone did not typically reach a critical depth for a stable pit to grow [151, 152]. It was found that, if a trench developed from a single cathodic particle intersected another cathodic particle, the pit can continue to grow after detachment of the first particle as the second particle can now undergo the same process [113, 117]. When the depth of the cluster-induced pit reaches a critical depth, the pit can propagate without further intersection of cathodic particles by an autocatalytic process [153, 154]. The absence of

84 IGC in the adjacent area also suggests that the larger- scale pitting was not induced by

IGC or grain fallout, which would then imply that the larger-scale pitting may be developed from coalescence of particle-induced shallow pits.

3.5.2.3 Combined Larger-scale Pitting and Pit-associated IGC

A combination of larger-scale pitting and pit-associated IGC was the predominant corrosion morphology in 6022-T4 and PB after CASS, ASTM G85-A2, and field exposure (Table 3.2). Susceptibility to IGC in 6022-T4 can be attributed to the excess Si with respect to the Mg/Si ratio that is required for the formation of Mg2Si phase (slightly below 2) [6, 8, 87, 88, 101]. Microsegregation of Si and an adjacent Si- depleted zone has been reported and correlated to the development of IGC due to microgalvanic effect between the noble Si at GBs and adjacent Al matrix [6, 88]. A previous study on

AA2024-T351 suggests that low pH and presence of Cl- at IGC front stimulate initiation of crystallographic dissolution of selected grains [127]. Compared with the wide-spread

IGC after field exposure in 6022-T4 (discussed in Chapter 2), only wisps of IGC fissures can be observed adjacent to pits (boxed area in Figure 3.20 and Figure 3.22). This is likely due to enhanced cathodic reactions in the acidified solutions of the tests [124] and deposition of Cu cations in the CASS solution [155]. With enhanced cathodic reaction, grain dissolution associated with IGC was also enhanced so that IGC fissures could be expanded and less obvious than in field exposure. This can also be used to explain the absence of IGC wisps in 6061-T4 after exposed to CASS and ASTM G85-A2 (Figure

3.20 and Figure 3.22).

85 In addition to grain dissolution, what is referred to as pit-associated IGC in this study may develop as a result of extensive IGC, which was mainly seen in 6061-T6 after field exposure and laboratory tests using acidified solutions (CASS and ASTM G85-A2), or with addition of hydrogen peroxide (ASTM G110). As shown in Figure 3.12d, Figure

3.20 and Figure 3.22, for these test conditions where pitting appeared to have faceted bottoms similar to the larger-scale pits in Figure 3.10f, it is very likely that grain fallout occurred leaving behind voids.

3.5.2.3 Summary on Test Condition

The pH of the solutions used in the testing procedures plays the most important role in the corrosion morphology of 6xxx alloys in the laboratory corrosion tests.

Corrosion morphologies of the 6022 alloys, either without aging or slightly aged, are sensitive to pH. In all tests using neutral NaCl solution, no remarkable corrosion or only shallow pitting was observed after a reasonable time of exposure. When acidified solutions were involved, these alloys were susceptible to large scale attack likely due to suppressed alkaline dissolution around IMPs and enhanced cathodic reaction. For 6061-

T6 and C26N-PB, corrosion morphology was not significantly altered by solution pH likely due to relative high susceptibility to IGC.

Wet/dry cycles exhibit less effect on the corrosion behavior than solution pH in the tests investigated in this study. The effect of wet/dry cycles on corrosion morphology of 6022-T4 and PB was negligible. When neutral pH solutions were used, shallow pitting was observed in both ASTM B117 and cyclic B117 test on the 6022 alloys. For 6061-T4

86 and T6 and C26N-PB tested in neutral pH solutions, wet/dry cycles of cyclic B117 and

GMW 14872 hindered the formation of larger-scale pitting that was observed in continuous spray testing ASTM B117. This is probably because, in spite of a similar total wet time to a continuous test, aggressive local chemistry at sites of localized attack cannot be maintained in the drying stage and pitting attack needs to reinitiate again in the next wet stage. This is supported by the fact that the same corrosion morphology was observed in continuous spray testing (CASS) and cyclic testing (ASTM G85-A2) for all alloys when acidified solutions were used. In addition, corrosion products in solution, such as Al(OH)3, likely deposit on the exposed surface during drying and hinder further corrosion in the next cycle.

The role of cathodic accelerators added in solutions used did likely accelerate growth of corrosion attack. The cathodic accelerator CuCl2, which was used in the CASS test, accelerated corrosion when compared to the other test using an acidified NaCl solution (ASTM G85-A2). Like in ASTM G85-A2, CASS produced large scale pitting in all alloys and IGC in all alloys except for 6061-T4, but 8-hour exposure in CASS was equivalent to at least 2 days of exposure in ASTM G85-A2 in terms of maximum corrosion depth for all alloys. For ASTM G110, which used the addition of peroxide as cathodic accelerated, it is hard to distinguish the role of the cathodic accelerator given the fact that testing was only conducted for up to 24 hours. Given that for 6061-T6 and

C26N-PB (Figure 3.23d-e), 24-hour exposure in ASTM G110 with addition of peroxide caused a maximum depth of attack deeper than 30-day exposure of ASTM B117. It is likely that the peroxide also accelerated the corrosion expected for the pH of that solution, which was neutral. All in all, it is likely that the acidic pH drives the 87 appropriate corrosion morphology and a cathodic accelerator can be used to speed up the result. Further testing would be needed to confirm this.

The fact that an acidic pH was the largest factor in reproducing what is seen in the field, as opposed to wet/dry cycling and cathodic accelerator, implies that pH is the controlling environmental factor in a true field environment. Pollutions in an industrial or urban atmosphere could lead to an acidic environment for automobiles [17, 32].

Therefore, the above analysis on the correlation between laboratory and field may be applied to automobiles operating in an industrial or urban environment.

3.6 Conclusions

In this study, the corrosion behavior of commercial Al-Mg-Si alloys in accelerated tests and was compared with the results from field exposure. Corrosion of Al-

Mg-Si alloys in field and laboratory corrosion tests usually exhibits a complex morphology depending on corrosion condition and alloy type. The ability of accelerated test or tests to simulate the corrosion morphology seen in the field was determined by using multiple methods including length/area ratios, fractal dimension, and a machine learning method. The following conclusions can be drawn:

⚫ The pH of solutions used in accelerated tests is the critical environmental variable

that controls corrosion morphology.

⚫ In laboratory tests using neutral solutions, shallow pitting likely induced by trenching

around cathodic particles (depth <10 µm) was the most commonly observed

corrosion morphology.

88 ⚫ Larger-scale pitting and pitting-associated IGC dominated on all alloys in salt spray

tests that use acidified NaCl solution (around pH 3). This attack likely developed

from isolated IGC followed by grain dissolution or grain fallout depending on the

susceptibility to IGC.

⚫ For engineering perspective, fractal analysis and L2/A ratio were able to distinguish

different corrosion morphologies so that they provide a quantitative way to evaluate

the ability of corrosion test to simulate the corrosion morphology in field.

⚫ None of the laboratory tests investigated in this study showed the exact same

corrosion morphology to that in field, but CASS an ASTM G85-A2, which utilized

an acidified NaCl solution, led to the most similar corrosion morphology.

89 Table 3.1: A comparison of key features of each accelerated test used in this study. Any solution additives besides NaCl is given in the appropriate column.

Type [Cl-] pH Additives Temperature Pull-out Times Environmental (M) (°C) Variable

ASTM G110 Immersion ~1 6 ~0.3 Vol.% 30±3 24 hours Cathodic H2O2 Accelerator

ASTM B117 Cont. 0.9 Neutral 35±2 2, 7,14, 30 days Neutral NaCl solution

ASTM B368 Cont. 0.9 3.1-3.3 0.25 g/L 49±1 8, 22, 44 hours Acidified NaCl (CASS) CuCl2·2H2O solution + Acetic acid Cathodic Accelerating Ions

Cyclic B117 Cyclic 0.9 Neutral 49±2 7,14, 30, 60 NaCl solution + days wet/dry cycle

ASTM G85- Cyclic 0.9 2.8-3 Acetic acid 49±2 2, 7,14, 30 days Acidified NaCl A2 solution + wet/dry cycle

GMW14872 Cyclic 0.2 Neutral 0.1 wt% CaCl2 Spray at 25±3 2, 7,14, 54 Complex salt 0.075 wt% Humid at 49±2 cycles solution + NaHCO3 Drying at 60±2 wet/dry cycle

90 Table 3.2: Summary of corrosion morphology in laboratory corrosion tests and field exposure. Corrosion morphology observed after 2 -year field exposure is also classified based on the result from Chapter 2. Boxes indicate alloys with similar corrosion behavior.

91

Table 3.3: Fitting parameters and adjusted R2 for power-law fits (depth = Ctm) of average maximum depth of attack, depth (µm), as a function of time, t (hours).

6022-T4 6022-PB C (µm/h) Slope - m Adjusted R2 C (µm/h) Slope - m Adjusted R2 Field 0.030 0.806 0.976 1.47 0.411 0.992 CASS 23.2 0.430 0.811 36.0 0.309 0.796 ASTM G85-A2 3.46 0.579 0.969 1.85 0.523 0.713 6061-T4 6061-T6 C (µm/h) Slope - m Adjusted R2 C (µm/h) Slope - m Adjusted R2 Field 0.338 0.500 0.771 2.01 0.418 0.903 CASS 34.4 0.236 0.999 52.1 0.402 0.801 ASTM G85-A2 1.79 0.585 0.939 32.7 0.300 0.695 ASTM B117 3.47 0.436 0.778 6.64 0.300 0.805 C26N-PB 2 C (µm/h) Slope - m Adjusted R Field 3.15 0.330 0.653 CASS 55.8 0.128 0.818 ASTM G85-A2 16.2 0.358 0.861 ASTM B117 1.65 0.525 0.992

92

Figure 3.1: Test setup for ASTM G110.

Figure 3.2: Coupon arrangement in the salt spray chamber.

93

Figure 3.3: Modified GMW14872 flow diagram.

94

Figure 3.4: Example of identification of individual localized attack sites.

Figure 3.5: Example of an a) original cropped image and the resulting images after image processing of b) binarization, c) edge detection, and d) resized.

95

Figure 3.6: Example of the resulting images of boxes covering the corrosion boundaries using different box sizes: a) box size = 1 pixel, N = 7695; b) box size = 2 pixels, N = 3980; c) box size = 4 pixels, N = 1972; d) box size = 8 pixels, N = 876; e) box size = 16 pixels, N = 327; f) box size = 32 pixels, N = 110.

Figure 3.7: Log-log plot of the number of boxes vs. the box size. 96

Figure 3.8: Acquisition of input images for fine-tuning of GoogLeNet.

97

Figure 3.9: Transfer learning of GoogLeNet: a) original GoogLeNet model; b) retaining; c) classification using re-trained GoogLeNet.

98

Figure 3.10: Optical micrographs showing 4 categories for localized attack observed on cross-sections after exposure: a) shallow pitting; b-c) larger-scale pitting; d) isolated IGC; e-f) pit-associated IGC.

99

Figure 3.11: Coupon surface after 24-hour exposure of ASTM G110 test for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N-PB.

100

Figure 3.12: Optical microscopy images of cross-section after 24-hour exposure of ASTM G110 test for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6 with a larger- scale pit and pit-associated IGC, e) 6061-T6 with isolated IGC and shallow pits, f) C26N-PB with larger-scale pits, isolated IGC, and pit-associated IGC, and g) C26N- PB with shallow pits.

101

Figure 3.13: Coupon surface and top-view optical micrographs after 30-day exposure of ASTM B117 test for a) 6022-T4, b)6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N-PB.

102

Figure 3.14: Representative optical microscopy images of cross-sections after ASTM B117 test for 2, 7, 14, and 30 days. 103

Figure 3.15: Coupon surface and top-view optical micrographs after 60-day exposure of cyclic B117 test for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N-PB.

104

Figure 3.16: Optical microscopy images of cross-section after 60-day exposure of cyclic B117 test for a) 6022-T4, b)6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N- PB. f) IGC in 6061-T6 after 7 days of exposure in cyclic B117.

105

Figure 3.17: Coupon surface and top-view optical micrographs after 54-cycle exposure of GMW14872 test for a) 6022-T4, b) 6061-T6, and c) C26N-PB.

106

Figure 3.18: Representative optical microscopy images of cross-sections after GMW14872 test for 2, 7, 14, and 54 cycles.

107

Figure 3.19: Coupon surface and top-view optical micrographs after 44-hour exposure of CASS test for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N-PB.

108

Figure 3.20: Representative optical microscopy images of cross-sections after CASS test for 8, 22, and 44 hours.

109

Figure 3.21: Coupon surface and top-view optical micrographs after 30-day exposure of ASTM G85-A2 test for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N- PB.

110

Figure 3.22: Representative optical microscopy images of cross-sections after ASTM G85-A2 test for 2, 7, 14, and 30 days. 111

Figure 3.23: Average maximum depth of attack that is greater than 10 μm as measured from cross-section images for a) 6022-T4, b) 6022-PB, c) 6061-T4, d) 6061-T6, and e) C26N-PB in ASTM G110, ASTM B117, ASTM G85-A2, and CASS. The lines are power-law fits to data from individual test environments where green triangles are for ASTM G85-A2, red circles indicate CASS, blue upside-down triangles indicate ASTM B117, cyan diamonds indicate ASTM G110, and black squares indicate field behavior. Field exposure results are replotted from Chapter 2 (Figure 2.16). The errors bars on all data points show one standard deviation from the average values. 112

Figure 3.24: Fractal dimension (FD) of corrosion front estimated using box-counting method for (a-b) 6022-T4, (c-d) 6061-T4, and (e-f) 6061-T6. The plots are the mean value of FD is plotted with the tips of the error bar indicating the maximum and minimum in the plots on the left. The arrows indicate the corresponding exposure times for the cumulative probability plot. On the right are cumulative probability of FD for the selected times of exposure.

113

Figure 3.25: Length/area ratio, L/A, of corrosion front estimated using box-counting method for (a-b) 6022-T4, (c-d) 6061-T4, and (e-f) 6061-T6. The plots are the mean value of L/A is plotted with the tips of the error bar indicating the maximum and minimum in the plots on the left. The arrows indicate the corresponding exposure times for the cumulative probability plot. On the right are cumulative probability of L/A for the selected times of exposure. 114

2 Figure 3.26: Length/area ratio, L /A, of corrosion front estimated using box-counting method for (a-b) 6022-T4, (c-d) 6061-T4, and (e-f) 6061-T6. The plots are the mean 2 value of L /A is plotted with the tips of the error bar indicating the maximum and minimum in the plots on the left. The arrows indicate the corresponding exposure times 2 for the cumulative probability plot. On the right are cumulative probability of L /A for the selected times of exposure.

115

Figure 3.27: Confidence of correlation (probability) of corrosion morphology in field exposure to corrosion tests for 6022-T4. Confidences for each sample (cross-section) were plotted as columns in the corresponding subplots.

Figure 3.28: Confidence of correlation (probability) of corrosion morphology in field exposure to corrosion tests for 6061-T4. Confidences for each sample (cross-section) were plotted as columns in the corresponding subplots.

116

Figure 3.29: Confidence of correlation (probability) of corrosion morphology in field exposure to corrosion tests for 6061-T6. Confidences for each sample (cross-section) were plotted as columns in the corresponding subplots.

117

2 Figure 3.30: Correlation of FD and L /A to corrosion morphology in 6022-T4 after accelerated tests and field exposure.

118

Figure 3.31: Correlation of FD and L2/A to corrosion morphology in 6061-T4 after accelerated tests and field exposure.

119

Figure 3.32: Correlation of FD and L2/A to corrosion morphology in 6061-T6 after accelerated tests and field exposure.

120

Figure 3.33: SEM of 6061-T6 after 7 days of exposure in ASTM B117.

121 4. Effects of Microstructure and Electrochemical Property on Corrosion Behavior of Al-

Mg-Si Alloys

4.1 Abstract

The goal of this chapter is to correlate microstructural features with corrosion behavior observed in Chapters 2 and 3. Large secondary phase particles were identified with SEM/EDS. Distributions of second phase particles (in terms of cluster size) were evaluated using SEM over a large area. Potentiodynamic polarization was conducted using an electrochemical microcell on bulk casts of the following secondary phases:

Mg2Si, Al5FeSi, AlFe(MnCr)Si, and Q-Al4Mg8Si7Cu2. Coarse Fe-rich intermetallic particles (IMPs) were the most common secondary phase associated with localized corrosion on 6022-T4, 6061-T4 and T6, and C26N-PB as the shallow pits could be mainly attributed to the coarse Fe-rich IMPs in all the tested alloys. However, a large majority of individual cathodic IMPs were not able to cause a stable pit because the trench created around the particle was too shallow to form a stable pit before removal of the particle. Possibility for the formation of stable pits increased with higher particle density and throwing power due to particle clustering. Susceptibility to IGC of 6061-T4 increased after artificial aging, but the formation of Cu-rich Q phase may not be responsible to the increased susceptibility owing to the similar OCP of Q phase to high- purity Al. 122

4.2 Objectives

Much of the prior research on the effects of microstructure of Al-Mg-Si alloys has been focused on the corrosion mechanism related to an individual feature under a certain environment [9, 11, 94, 104, 156, 157]. In other words, there is a gap in the current literature concerning direct evidence that relates the secondary phases to the corrosion morphologies observed in automotive field environment. In order to understand the long-term corrosion behavior in an automotive environment and improve the capability of accelerated tests to simulate field behavior, it is necessary to understand the interaction between different microstructural features and environmental variables.

Therefore, the goal of this section is to answer the following questions:

• What is the role of coarse intermetallic particles (IMPs) in corrosion of Al-Mg-Si

alloys in the solutions used in accelerated tests from Chapter 3?

• Given the fact that artificial aging of Al-Mg-Si alloys leads to the formation of

precipitates at grain boundaries, what is the role of β-Mg2Si, Q-phase, and the

precipitates free zone (PFZ) at GBs?

In order to answer the above questions, the following objectives were formulated:

• Identify the intermetallic particles in Al-Mg-Si alloys through either SEM or the

literature

• Investigate the electrochemical properties of these intermetallic particles using

microcell

• Understand the effect of clustering of intermetallic particles on localized corrosion 123 • Investigate the effect of formation of β-Mg2Si and Q-phase at grain boundaries

• Correlate the microstructural features and electrochemical properties with the

corrosion behavior observed in the accelerated tests from Chapters 2 and 3

4.3 Experimental Procedure

4.3.1 Metallurgical Characterization

4.3.1.1 Particle Characterization

Intermetallic particles with a dimension of several microns in wrought 6022-T4,

6061-T4, 6061-T6, and C26N-Paintbaked (PB) were investigated using SEM/EDS.

Details of the chemical compositions and tempering condition for all alloys are given in

Section 2.3. Samples of the L × T surface and L x S cross-section of each alloy were first abraded up to 1200 grit with SiC paper in water and polished using 3 and 1 µm diamond paste. Reagent grade ethanol was used for polishing to prevent the surface from corrosion. The definition of the L × T surface and L × S cross-section is given in Section

2.3. Intermetallic particles on the polished surfaces were distinguished by the brightness on the backscattered SEM images using FIJI software [123]. The type of IMPs and approximate atomic percentage of the elements in each type were determined by point measurement using EDS. The size and distribution of each type of particle were investigated on the L × S cross-section for cluster analysis.

124 4.3.1.2 Cluster Analysis

Cluster analysis was conducted on L × S cross-sections. Figure 4.1 shows the process for particle clustering analysis. As shown in Figure 4.1a, a large cross-sectional

SEM image of the L × S surface was obtained by stitching the individual images together. The IMPs were then identified by the brightness differing from the Al matrix

(Figure 4.1b). After the particles were successfully identified, each particle was fit to an ellipse using FIJI [123]. The fit ellipses were plotted using Matlab as shown in Figure

4.1c.

The size of particle clusters depends on the distribution (density) and the

“throwing power” of IMPs. In this work, throwing power of a cathodic particle is defined as the distance from the particle within which Al matrix corrodes. To evaluate the effect of throwing power on cluster size, the fit ellipses were expanded by multiplying the major and minor axes with a scaling factor, m. The expanded ellipses were then replotted at the same location and orientation. As a result, the overlapping ellipses can form clusters. The depth of the cluster in any one direction (S direction) that can then be equated to the depth of a pit. The height of the resulting clusters of ellipses along the S- direction was used for analysis because the pit depth was measured in the S-direction when the L × T surface was exposed in laboratory and field exposure.

4.3.2 Optical Profilometry

Optical profilometry (OP) was conducted on samples exposed to ASTM B117 and GMW14872 from Chapter 3 using a Veeco Wyko NT1100 optical profilometer. 125 These two tests were selected because shallow pits dominated in the tests and and relatively clean surfaces can be obtained after cleaning. Individual pits were isolated and identified using a depth threshold of 1 µm. Random regions with a total minimum area of

5 mm2 were investigated on each sample.

4.3.3 Electrochemical Characterization

4.3.3.1 Potentiodynamic Polarization

In order to investigate the effect of solution chemistry on the electrochemical behavior of bulk Al alloys, potentiodynamic polarization was conducted using the bulk solutions used in the accelerated tests. The solutions were prepared prior to experiments following the same procedure as described in Section 3.3.1, i.e. 5 wt.% (about 0.9 M)

NaCl (ASTM B117), 5 wt.% NaCl at pH 3 (ASTM G85-A2), 5 wt.% NaCl at pH 3 with

2+ Cu (CASS), 1 M NaCl with H2O2 (ASTM G110), and a mixed salt solution of NaCl,

CaCl2, and NaHCO3 (GMW14872). The Al alloys involved in electrochemical measurement were 6022-T4, 6061-T4 and T6, and C26N-PB. 6022-PB, which was tested in accelerated corrosion tests in Chapter 3, was not used for polarization scanning because corrosion behavior of 6022-PB was same to that of 6022-T4, so only 6022-T4 was used here. Samples for electrochemical measurements were cut from the Al sheets and prepared by grinding the rolled surface to 1200 grit SiC paper using deionized water

(before 1200 grit) and reagent grade ethanol (1200 grit) as lubricants. All the polarization tests were performed for at least 3 times for each condition using a Gamry Reference 600 potentiostat. 126 Potentiodynamic polarization was carried out at room temperature using a regular vertical cell with a hole at the bottom that exposed a circular area of 1.27 cm in diameter to the solution. A rubber o-ring was used between the sample and the vertical cell. The solution was left open-to-air in a “quiescent” condition during the experiments and the distance between the sample to the solution surface was about 5 cm. A three-electrode setup was used with a saturated calomel electrode (SCE) serving as a reference electrode and a graphite rod serving as the counter electrode. Anodic and cathodic polarization were conducted separately at a scan rate of 0.167 mV/s after 1-hour immersion at open circuit potential (OCP).

4.3.3.2 Cyclic Polarization in Deaerated 5 wt.% NaCl Solution

Cyclic polarization was conducted in a deaerated 5 wt.% NaCl solution to identify the pitting potential (Ep) of the Al alloys. Samples were prepared following the same procedure as described in the previous section. The cyclic tests were carried out in a sealed flat cell utilizing a three-electrode setup with an SCE reference electrode and Pt counter electrode. The solution was deaerated with argon gas for at least 2 hours before being transferred to the flat cell for testing. The system was stabilized for 30 minutes followed by an anodic polarization at a rate of 0.5 mV/s started from 30 mV below OCP.

Anodic scanning was reversed when the measured current reached 0.1 mA. The pitting potential was identified as the potential after which the measured current increased sharply. The solution was continuously purged with argon gas during the tests.

127 4.3.3.3 Potentiodynamic Polarization on Cast Surrogates of Phases using

Electrochemical Microcell

In order to evaluate the microgalvanic effect between secondary phases and the Al matrix, polarization behavior of the secondary phases needs to be investigated separately when the Al matrix is not inherently coupled. Therefore, cast surrogates of typical second phases were made by ACI Alloys with the stoichiometric ratio reported in the literature

[95, 158]. An arc melter filled with inert gas (argon) was used to mix high-purity elemental metals to obtain ingots of Q-phase (Al4Cu2Mg8Si7), β-phase (Mg2Si), and Fe- rich α-phase (Al5FeSi). In addition, since a small amount of Mn or Cr can exist in the α- phase to substitute Fe, Al75(Fe13MnCr)Si10 and Al75(Fe13Mn2)Si10 surrogates were also made to study the effect of these elements on electrochemical properties. As shown in

Figure 4.2, modeled solidification curves suggested that it is difficult to obtain homogeneous cast surrogates of the Q-phase and Fe-rich phases because other phases solidify at higher temperatures than the target phases. In fact, previous studies using transmission electron microscopy (TEM) revealed that the phases in the bulk surrogate alloys do not have a uniform structure but do have approximate overall compositions as mentioned above [95, 158]. High-purity Al (99.99%) was also investigated in comparison to the secondary phases.

Due to the phase separation and high porosity of the cast surrogates, a regular flat cell is not suitable to perform electrochemical measurements on the target phases.

Instead, a microcapillary electrochemical cell (microcell) was used to investigate the polarization behavior [93, 159, 160]. A three-electrode setup was used for the

128 electrochemical microcell (Figure 4.3). The counter electrode was a Pt wire and an SCE electrode was used as the reference electrode. Due to the small exposed area and low measured current, the entire apparatus was put in a Faraday cage to reduce noise during polarization. A Gamry Reference 600 potentiostat was used for all experiments. A glass capillary tube was heated in the middle and pulled at both ends to produce a sharp capillary tip with an inner diameter of 50 to 100 µm. Before the experiments, the microcell was filled with test solution and placed on a spot of the target phase with the help of an optical microscope. Within the bulk surrogate alloys, there were locations of the target phase that were large enough to allow microcell characterization without the influence of other phases. The crevice between the capillary tip and the sample surface is sealed with silicone rubber coated on the tip.

In advance of electrochemical measurements, the compositions of the target phases were confirmed by using SEM/EDS. The casts of the three Fe-rich phases had a dendritic structure. Figure 4.4 shows the backscattered SEM images of the Al5FeSi and Q phase samples. EDS result shows that the brighter dendrites were enriched with Fe and the space among the dendrites was filled with high-purity Al. Polarization with microcell was performed on the bright dendrites because they have similar composition than the designed Al5FeSi phase and the surface area of the dendrites was large enough for the capillary. The surrogate of the Q phase had a more refined structure; the brighter area was enriched with Al and Cu, while the darker area had higher contents of Mg and Si. Due to the small scale of the segregation, the exposed area for microcell polarization was not a uniform phase so a relatively larger variation on the measured polarization curves was expected. 129 After the target phase was identified in the ingots, the samples were abraded to

1200 grit SiC paper in alcohol for microcell. Anodic and cathodic polarization were performed at a scan rate of 1 mV/s after the initial stabilization. Figure 4.5 shows examples of the exposed regions after anodic polarization. The tip size can be determined by the corroded area circled in red. At least three scans were conducted in each test solution. The test solutions used in this work were 5 wt.% NaCl, 5 wt.% NaCl acidified to pH3 with acetic acid, and a mixed solution of NaCl, CaCl2, and NaHCO3, which was prepared following the procedure of GMW14872 as described in Section 3.3.

4.3.4 Accelerated Corrosion Testing of 6061-T6’

To investigate the effect of artificial aging, the original 6061-T4 sheet was aged at

160 °C for 180 hours to obtain a T6 version of 6061, which is referred to as 6061-T6’. A better comparison can be made because the 6061-T4 and 6061-T6’ sheets were from the same lot. Laboratory corrosion tests were conducted on 6061-T6’ following the same procedure as described in Section 3.3. After exposure, coupons were cross-sectioned to reveal the corrosion morphology.

130 4.4 Results

4.4.1 Characterization of Intermetallic Particles

4.4.1.1 Particle Identification

Figure 4.6 shows the backscattered SEM of a L × T surface of 6022-T4 (a),

6061-T4 (b), 6061-T6 (c), and C26N-PB (d). Due to different chemical compositions, the second phase particles present different brightness than the Al matrix. It can be seen that

IMPs were randomly distributed on the surface. Some of the IMPs were closer to other particles and formed clusters.

The type of the second phase particle was determined using EDS (Figure 4.7).

The bright ones are the Fe-rich particles and the dark ones are enriched with Mg and Si.

The Fe-rich particles had significantly higher Fe and Si than the Al matrix with a small amount of Mn and Cr. The Fe-rich intermetallic particles can be observed in all the alloys. The darker β-Mg2Si particles can only be found at the size observable in an SEM in 6061-T4 and T6. The high peak of Al detected in the spectrum of β-Mg2Si particles is likely from the underlying matrix. Note that precipitates and precipitate free zones produced during aging cannot be detected using SEM. TEM would be required to investigate metallurgical changes with aging/paintbaking, but was outside the scope of this work.

131 4.4.1.2 Cluster Size and Throwing Power

In order to investigate the relationship between cluster size and scaling factor, m, for each alloy, cumulative probabilities of the cluster height in the short transverse direction (S) were plotted with examples of m factor of 1, 3, and 5 in Figure 4.8. The height in the S direction is used because it is the direction of pit growth when the L × T surface is exposed. Therefore, the cluster height can be correlated with the depth of pits on the L × T surface. When m = 1, the height plotted in Figure 4.8 is the height of the ellipse fit to an individual particle.

With increasing m, the cumulative probability curves of cluster height move to the right for all the alloys. It can be seen that, although all alloys have the same type of

IMPs, coalescence of particles occurs at different m due to different size and density of

IMPs. When coalescence of two particles occurs, the height would increase much greater than what would be expected due to expansion of the fit ellipse with increased m alone.

This is due to the height of the coalescing as a second particle adding to the previous cluster. Table 4.1 shows the height of clusters at the 95th percentile. For 6022-T4, the height of clusters at the 95th percentile only slightly increased when m increases from 1 to

3, while a larger increase can be seen from m = 3 to 5. An opposite trend can be observed in 6061-T4 and T6; the height increases faster from m = 1 to 3 compared with 3 to 5. This is likely because particle clusters can form in the 6061 alloys with a lower m than 6022.

132 4.4.2 Characterization of Shallow Pitting using Optical Profilometry

Although OP is not able to detect undercutting and intergranular corrosion, near- hemispherical shallow pits can still be characterized accurately. Figure 4.9 - Figure 4.14 display the relative and cumulative probability of pitting depth and area measured by OP after ASTM B117 and GMW14872 – the test in which shallow pits were the most commonly seen corrosion morphology (Chapter 3). 6022-PB and 6061-T4 behaved similarly to 6022-T4 in ASTM B117, therefore OP was conducted only on 6022-T4,

6061-T6, and C26N-PB.

Figure 4.9 - Figure 4.11 show the probability of pit depth measured by OP after 2 and 30 days of exposure in ASTM B117 in 6022-T4 (Figure 4.9), 6061-T6 (Figure 4.10), and C26N-PB (Figure 4.11). The relative probability of pit depth and area can be seen to decrease exponentially as the depth and area become larger. Over 98% of the pits observed after ASTM B117 for 30 days had a depth lower than 15 µm in all the alloys

(Figure 4.9 - Figure 4.11 b) Additionally, neither pit depth (Figure 4.9 - Figure 4.11 a and b) nor pit area (Figure 4.9 - Figure 4.11 c and d) was significantly altered by time in the exposure.

Figure 4.12 - Figure 4.14 show the probability of pit depth measured by OP after

14 and 54 cycles of exposure in GMW14872 in 6022-T4 (Figure 4.12), 6061-T6 (Figure

4.13), and C26N-PB (Figure 4.14). No pits were detected on the three alloys with depths larger than 15 µm even after 54 cycles of exposure. Relative probability of pit depth decreases with increasing depth. After 54 cycles of exposure, relative probabilities of pits lower than 2 µm decrease compared with 14 days, while for pits deeper than 2 µm, the

133 probabilities slightly increase. This implies that shallow pits grow slightly during exposure with little to no new pits forming. It is notable however that these shallow pits don’t tend to grow into severe pits over the time tested. The probability distribution of the pitting area was not significantly altered by increasing exposure time.

4.4.3 Electrochemical Characterization

4.4.3.1 Potentiodynamic Polarization

Figure 4.15 - Figure 4.18 show the representative potentiodynamic polarization curves of the as-received 6022-T4 (Figure 4.15), C26N-PB (Figure 4.16), 6061-T4

(Figure 4.17), and 6061-T6 (Figure 4.18), in the bulk solutions used to simulate those that were used in the accelerated corrosion tests. The solutions and corresponding accelerated corrosion tests were as follow: 0.9 M NaCl + Cu2+ +pH3 for CASS, neural pH 0.9 M

NaCl for ASTM B117, 1 M NaCl + H2O2 for ASTM G110, 0.9 M NaCl + pH3 for ASTM

- G85-A2, and 0.2 M [Cl ] + NaHCO3 for GMW14872. Each test was repeated at least three times with good repeatability among different scans. The measured pitting potentials and OCPs are summarized in Table 4.2. The corresponding corrosion current densities are given in Table 4.3.

In quiescent solutions, no pitting potential is observed on the anodic polarization scan for all alloys investigated. Pinning of the pitting potential at OCP is typical of Al alloys, which typically require a deareated environment for true identification of the pitting potential. This is confirmed by the cyclic polarization curve in deareated 5 wt.%

134 NaCl solution (green dash) that reveals that the pitting potentials of the tested Al alloys were about -0.7 V vs. SCE, which was close to the OCPs measured in the quiescent solutions. Considering the lack of a passive region in quiescent solutions and the hysteresis loop observed in cyclic polarization (green dash), the tested Al alloys were susceptible to pitting attack in the presence of oxygen. The OCP and anodic polarization curves were consistent for the solutions with about 1 M [Cl-]. The anodic polarization curve in the GMW14872 solution, 0.2 M NaCl with 0.075 wt.% NaHCO3 added (purple), shows higher OCP for about 40 mV and lower icorr than the other solutions. No explicit pitting potential was observed in the GMW 14872 solution except for 6022-T4 (Figure

4.15), which exhibited a slight inflection point on the anodic curve at about -0.65 V vs.

SCE. This may indicate a higher pitting potential than in the other solutions for 6022-T4.

The limiting current of the cathodic reaction varied with solution chemistry, with the highest cathodic current density observed in solutions adjusted to pH 3 or with peroxide additions. When considering solutions at pH 3 (red and cyan), the cathodic

- reaction was further enhanced with the addition of CuCl2 (cyan). In spite of different [Cl ]

, the cathodic polarization curves were close to each other in neutral solutions without the addition of peroxide (green and purple).

For all the alloys except for 6061-T6, the highest icorr were obtained in acidified solution with the addition of CuCl2 (CASS). For 6061-T6, icorr in the CASS solution was only slightly lower than that in G110 solution. Without acidification and cathodic accelerator, the B117 and GMW solution led to much lower icorr than the other solutions.

135 4.4.4.2 Microcell

Figure 4.19 displays representative potentiodynamic polarization curves of cast surrogates of second phases and pure Al measured using the microcell. The mixed salt solution of NaCl, CaCl2, and NaHCO3 was the solution used in GMW14872. During the anodic polarization, a breakdown potential was usually observed on pure Al and the three

Fe-rich phases in the pH neutral and acidified NaCl solution. Additional polarization scans were conducted on pure Al in the acidified NaCl solution but only a very low current density with large noise was obtained. The reason for this phenomenon is not clear, but it may be due to corrosion product blocking the capillary tip in an acidified solution. In the mixed salt solution, a breakdown potential was observed on the pure Al, while the Fe-rich phases tends to maintain passivation below 0.2 V vs. SCE.

A relatively large variation was observed on the measured polarization curves.

Therefore, the average OCPs and corrosion currents of tested phases were plotted in

Figure 4.20. The pure Al and Q phase had similar OCP at about -0.7 V vs. SCE, while the

Q phase exhibited the highest corrosion current of over 10-5 A/cm2. In all the test solutions, the Fe-rich phases behaved similarly with an average OCP of about -0.6 V vs.

SCE and corrosion current of 10-7 mA/cm2. The addition of a small amount of Mn and Cr in the Fe-rich phase did not have a significant effect on the electrochemical properties.

Note that in the acidified NaCl solution, the average OCPs of all the phases except for β-

Mg2Si were closer to each other than in the other solutions. The average OCP of the β-

Mg2Si phase was about -1.4 V vs. SCE in the 5 wt.% NaCl solutions, while, in the mixed salt solution, the OCP was found to be higher at about -1 V vs. SCE.

136

4.4.5 Corrosion morphology of 6061-T6’ in Laboratory Corrosion Tests

As microcell is not capable of the resolution needed to capture grain boundary effects and TEM to investigate grain microsctructure was not in the scope of this program, effects of microsegregation at GBs were inferred by comparing the corrosion morphology of 6061-T6’ and 6061-T4 in laboratory corrosion tests. Formation of secondary phases is expected after artificial aging. Therefore, the effect of precipitates and precipitate free zones at grain boundaries on corrosion morphology can be investigated by comparing the T6 and T4 temper of the same alloy. While aging to T6 will introduce strengthening precipitates into the matrix, it will also increase the amount and size of those phases on grain boundaries, which will increase the size of precipitate free zones as well.

For 6061-T6’, it can be seen in Figure 4.21 that IGC was the predominant corrosion morphology in all the tests. Shallow pits can be observed on the cross-section after ASTM G110 and ASTM B117, and IGC in these tests only propagated into the alloys for the depth of several grains (Figure 4.21a-b). ASTM G85-A2 and CASS led to more severe IGC in 6061-T6’ (Figure 4.21c-d). The severe pits in ASTM G85-A2 and

CASS tests can be attributed to grain fallout due to the IGC.

4.5 Discussion

In this portion of the research, experiments were conducted to determine why specific corrosion morphologies were observed in the results shown in Chapters 2 and 3. 137 Overall, the following, which will be explained in detail in this discussion, has been determined. Artificial aging from T4 to T6 enhanced IGC in 6061 likely due to formation of PFZs at grain boundaries. Shallow pitting likely developed from trenching around a single cathodic IMP due to higher pH and microgalvanic effect. With a higher density of

IMP and low susceptibility to IGC, larger-scale pitting on 6061-T4 can be likely attributed to particle clustering. Enhanced grain dissolution observed in CASS and

ASMT G85-A2 test is likely due to increased cathodic reaction in lower pH solution.

Low pH also likely led to destabilization of oxide on IMP surfaces, but an investigation of the stability of oxide on IMPs was out of the scope of this program.

4.5.1 IGC from Precipitates and Precipitate Free Zones at Grain Boundaries

The formation of precipitates and precipitate free zones (PFZs) through artificial aging has been well established to increase the susceptibility to IGC [9, 10, 88, 101-103,

112, 161, 162]. Because TEM was not feasible in this research, and there is copious literature evidence on the effect of aging on precipitation of strengthening precipitates in these alloys, the effect of artificial aging in accelerated corrosion tests was investigated by comparing corrosion morphology of 6061-T4 and 6061-T6’. As mentioned in Chapter

3, 6061 in the T4 temper exhibited no susceptibility to IGC in the tests using neutral solutions. However, after aged to the T6 temper, IGC can be observed after ASTM G110 and ASTM B117 (Figure 4.21 a-b). In the tests using acidified NaCl solution, ASTM

G85-A2 and CASS, IGC can be observed in 6061-T4, but grain dissolution dominated

(Chapter 3). In contrast, IGC dominated in 6061-T6’ as indicated by the clear IGC

138 crevices and grain fallout (Figure 4.21 c-d). The corrosion morphologies of 6061-T6’ in accelerated corrosion tests were consistent with those of 6061-T6 (Chapter 3).

Microsegregation at grain boundaries of heat treatable Al alloys is generally accepted to be responsible for the increased susceptibility to IGC after artificial aging due to the formation of precipitates and PFZ and the electrochemical potential difference between the precipitates and PFZ [9, 10, 88, 101-103, 112, 161, 162]. With regard to Al-

Mg-Si(-Cu) alloys, typical phases that precipitate out during artificial aging include the

Cu-rich Q phase, Cu nanofilms, and β-Mg2Si. As shown in Figure 4.20, the OCP of the Q phase was close to high-purity Al. This would imply that the overpotential between the Q phase and PFZ is not large enough to be a strong driving force for IGC. In fact, prior research on Cu containing Al-Mg-Si alloys reveals that the Q-phase precipitates are inert as cathodes and can serve as obstacles that hinder IGC growth [162, 163]. Given this, the increased susceptibility to IGC of 6061 in the T6 temper can most likely be attributed to the Cu nanofilm and solute-depleted zone (SDZ) that forms at grain interfaces, which has also been reported in the literature to lead to enhanced IGC and lead to enhanced IGC [8,

88, 162].

Although the susceptibility to IGC primarily depends on Cu-rich phases [88], it was found that MgSi and Si precipitates can form at grain boundaries and lead to the formation of PFZs and microgalvanic couples at grain boundaries [8, 162]. Larsen et al.

[8] reported that slight IGC was observed on an essentially Cu-free Al-Mg-Si alloy with excess Si in both the T4 and the T6 temper. SEM investigation revealed that Si and

MgSi-phases likely formed after homogenization followed by air-cooling [8]. This explains the susceptibility of 6022-T4, which has a low Cu content of ~0.05 wt.%, to IGC 139 during field exposure and laboratory tests using acidified solutions (Chapters 2 and 3). It is an interesting observation that IGC was not observed in 6022-T4 in laboratory tests using neutral pH solutions, e.g. ASTM B117. This may suggest that an acidified electrolyte is likely required to include IGC in 6XXX Al alloys with Cu content as low as

~0.05 wt.%. This is supported by the reduced overpotential between the Al matrix (pure

Al) and the Fe-rich phases in acidified solution when compared to the pH neutral NaCl solution as indicated in Figure 4.20 a and b. Combined with the fact that trenching around the Fe-rich IMPs tends to be hindered by lower pH [150], less cathodic current would be consumed by the growth of shallow pitting, while more cathodic current would be available for the growth of IGC.

4.5.2 Trenching and Pitting from Coarse Fe-rich IMPs

4.5.2.1 Small Scale Pitting of Less than ~10 µm

IMPs in Al alloys are well known to contribute to localized corrosion. The type of large-scale IMPs in the automotive Al-Mg-Si alloys was determined by using SEM/EDS.

It can be seen that the predominant larger IMPs in the test alloys were enriched with Fe and Si with a small amount of Mn and Cr (Figure 4.7). Electrochemical properties of the

Fe-rich phases were investigated by potentiodynamic polarization on cast surrogates of these phases using a capillary microcell. It was found that the OCP of Fe-rich phases was higher than high-purity Al in the corresponding solutions (Figure 4.20). As a result, assuming that the electrochemical properties of high-purity Al were similar to the Al- matrix in the PFZ, the Fe-rich phases are cathodic to the Al matrix. The enhanced oxygen 140 reaction (ORR) kinetics on these phases may increase the pH of the electrolyte around the

Fe-rich particles and lead to the dissolution and trenching of the directly surrounding Al matrix owing to the breakdown of passivity. Therefore, the IMPs in the Al-Mg-Si alloys were likely responsible for the initiation of shallow pitting attack when exposed to a neutral electrolyte.

A cathodic particle-induced pit can cease to grow after the removal of the particle due to trenching beneath the particle. The transition of a cathodic trench to a shallow pit is a complicated and unknow process [154]. The exact critical depth of transition, at which a small shallow pit into a stable pit, is difficult to measure. An effort has been made on 2024-T3 to measure the transition size after 30 minutes of immersion in 5 %

NaCl solution and, assuming a hemispherical pit, the average diameter of small stable pits and large metastable pits was about 15 µm, which means a depth of about 8 µm

[148]. As shown in Table 4.1, the height of over 95% of the particles (m = 1) in 6022-T4 and C26N-PB was all below this value. Therefore, the majority of trenches resulting from particle-induced attack would not continue to grow during exposure. This observation is consistent with the pit depth measured by OP during exposure in ASTM B117 and

GMW14872 ( Figure 4.9 - Figure 4.14 a and b).

4.5.2.2 Effect of Particle Clustering

Stable pits that continue to grow during exposure have been attributed to the coalescence of pits from trenching around cathodic IMPs when the IMPs are clustered

[113, 116-118]. Harlow et al. fit the distance between particles with a Weibull

141 distribution and proposed a probability model for the cluster size depending on the distribution of IMPs and a critical distance between them [117, 118]. In this research, the goal was not to propose a model for the growth of pitting induced by particle clusters, but rather to correlate the corrosion behavior in accelerated corrosion tests (Chapter 3) with the microstructure of the studied alloys. Therefore, only the effect of particle distribution and throwing power on corrosion morphology will be discussed.

The throwing power of IMPs may vary with test solutions and environmental factors. The difference in pit area (Figure 4.24) may indicate the difference in corroded area related to IMPs and the throwing power of the particles. Heights of the cluster- induced attack increase with the increasing throwing power of IMPs. For example, with a throwing power of m = 3, over 5 % of the clusters in 6061-T4 and T6 can lead to stable pits that have depths higher than the approximate critical depth of 13 µm. Attempts have been made to correlate pit area with the throwing power, m, in ASTM B117 (Figure

4.22). It can be seen that distributions of pit area in ASTM B117 well matches that of cluster height with certain m.

The schematic diagram in Figure 4.23 shows the effect of particle density and throwing power on the formation of particle clusters. The relationship can be used to explain the corrosion behavior of 6022-T4 and 6061-T4 and T6 in ASTM B117 and

GMW14872 salt spray tests (Chapter 3). Severe pits are only predicted to occur with higher particle density and throwing power. According to the depth of pitting corrosion measured by optical profilometry shown in Figure 4.24, pit area on the exposed surface in

ASTM B117 is higher than that in GMW14872. Additionally, large scale pitting was only observed in ASTM B117. These two tests were used because they used different 142 solutions and shallow pitting was the dominant corrosion morphology in these tests. For the alloy with low particle density, 6022-T4, only shallow pits developed in GMW14872 and ASTM B117. With the higher particle density in 6061-T4/T6 (Table 4.4), severe pits

(>15 µm in depth) were able to form in ASTM B117 (Figure 3.14). This supports the hypothesis that smaller scale pitting on the order of 10 μm or less was due to trenching around cathodic IMPs that were far spaced and microstructures that have denser particle clustering can more readily facilitate the transition into deeper larger pits.

4.5.3 Proposed Corrosion Mechanisms in Exposure

Figure 4.25 displays a schematic diagram of the proposed relationships between essential microstructural features (in boxes) and corrosion morphologies (in ellipses) observed in the laboratory and field exposure (Chapters 2 and 3). Results from this work and previous studies [4-6, 8, 70, 86, 101, 164] on Al-Mg-Si alloys revealed that IMPs, microsegregation at GB, and grains with high stored energy/dislocations are most likely the driving force for shallow pitting, IGC, and crystallographic corrosion, respectively.

Prior research on 2XXX alloys confirmed that, below a critical size (approximately 4 nm wide) second phase precipitates do not perform as a separate electrochemical entity and pitting corrosion is not be facilitated by the precipitates [84, 165, 166]. Therefore, although the nanosized particles in grains (i.e. β’ and β’’ phases) will have an impact on the matrix composition and may change the corrosion potential and dissolution rate of the grain matrix, they do not likely serve as the driving force for initiation of localized corrosion on the 6xxx alloys. This is supported by the much larger scale of individual

143 sites of attack (at least several microns in depth) even at early stage of exposure as discussed in Chapters 2 and 3.

In Figure 4.25, the resulting corrosion morphology can be a combination of two or more corrosion morphologies depending on alloy microstructure and test environment.

Table 4.5 presents the proposed sequences of development of different corrosion morphologies observed in field and laboratory exposures combining the results from

Chapter 2 and 3. In the column of proposed pathway, the microstructural features and corrosion mechanisms involved are ordered from left to right following the proposed sequences for the resulting corrosion morphologies (rightmost in the column). The corresponding alloy or alloys and test environment for each pathway are given in the following columns. Shallow pits were the most common corrosion morphology in a neutral electrolyte, and can be attributed to the coarse Fe-bearing IMPs (#1-4 in Table

4.5). For 6061-T4, the transformation from a shallow pit to a severe pit (called “larger- scale pitting” here) was enabled when the size of particle clusters exceeded the critical depth for a stable pit (#2). As discussed in Chapter 3, initiation of IGC was likely due to the aggressive local chemistry in the shallow pits (#3-4) in 6061-T6 and C26N-PB. In addition, larger-scale pitting and pit-associated IGC were observed on 6061-T6 and

C26N-PB in ASTM B117, but not observed in GMW14872. This is likely due to crystallographic corrosion and grain dissolution initiating from IGC front owing to low pH and high concentration of Cl- at IGC front (#4) [127]. In contrast, larger-scale pitting was not observed in the cyclic GMW14872 likely because the aggressive local chemistry cannot be maintained due to drying stages (#3).

144 IGC of 6xxx alloys can be attributed to the microsegregation at grain boundaries due to formation of second phases and PFZ (#5-7). Note that susceptibility to IGC can be increased by artificial aging, but is not necessarily related to the formation of the Cu-rich

Q-phase. Based on discussion in Chapter 3, the larger-scale pitting and pit-associated

IGC in 6061-T6 could be a result of grain fallout due to fast growth of IGC (#5). This is supported by the small line segments observed in the cavities on 6061-T6 and the highest depth of attack among the tested alloys after the same period of exposure. On the other hand, grain dissolution instead of IGC was involved in the development of pit-associated

IGC in other alloys with lower growth rate of localized attack (#6-7). Grain dissolution was enhanced likely by increased cathodic reaction in the tests using acidified solution and/or addition of cathodic accelerator. In addition, as discussed in Chapters 2 and 3,

6022-T4 and PB and C26N-PB exhibited medium susceptibility to IGC. Therefore, both larger-scale pitting due to grain dissolution and wisps of IGC were observed (#6). For

6061-T4, only larger-scale pitting was observed due to low susceptibility to IGC and, thus, grain dissolution dominated (#7).

4.5.4 Connection between Microstructure and Electrochemistry and Correlation

between Field and Lab Testing

In this study, attempts have been made to understand the corrosion mechanisms involved in field and laboratory exposure and correlate laboratory to field environment with regard to the development of localized attack. As indicated in Table 4.5, it was proposed that localized attack in field exposure followed the same pathway as CASS and

145 ASTM G85-A2 (#5-7). The results from quantitative analysis of corrosion morphology in

Chapter 3 show that the corrosion morphology observed in field exposure had larger L2/A ratios than those in CASS and ASTM G85-A2 with similar depth of attack, which suggests that IGC was more significant than in these two tests. This is likely due to enhanced grain dissolution owing to faster cathodic reaction rate in acidified solutions

(Figure 4.15 - Figure 4.18).

4.6 Conclusions

• Shallow pits can be attributed to trenching around the coarse Fe-rich IMPs that are

cathodic to the Al matrix in all the tested alloys and MgSi particles

• It is proposed that the development of the shallow pits can be suppressed in an

acidified NaCl solution because the acidified solution neutralizes the OH- produced

at cathodic particles.

• An individual cathodic IMP may not be able to cause a stable pit because the trench

around the particle cannot grow to exceed the critical depth before the removal of

the particle. The possibility for the formation of stable pits increases with higher

particle density and throwing power due to particle clustering.

• Susceptibility to IGC of 6061-T4 increases with artificial aging, but the formation

Cu-rich Q phase is not necessarily related to the increased susceptibility owing to

the similar OCP of Q phase to high-purity Al.

146 Table 4.1: Cluster depth with m = 1, 3, and 5 in µm at the ~95th percentile.

m 1 3 5 6022-T4 5.5 8.3 17.0

6061-T4 7.5 24.4 69.5 6061-T6 9.9 26.0 52.5 C26N-PB 5.3 9.2 16.4

Table 4.2: Pitting potential Ep measured in deaerated 5 wt.% NaCl solution and open circuit potential in quiescent solutions of G110 2+ - (1 M NaCl + H2O2), B117 (0.9 M NaCl), G85-A2 (0.9 M NaCl + pH 3), CASS (0.9 M NaCl + Cu + pH 3), and GMW (0.2 M [Cl ] + NaHCO3). Unit: mV vs. SCE.

Ep ASTM G110 ASTM B117 ASTM G85-A2 CASS GMW14872

6022-T4 -715.6 ±4.9 -727.1 ±1.9 -726.2 ±12.1 -723.9 ±0.5 -725.8 ±0.5 -683.8 ±11.5 6061-T4 -704.5 ±2.2 -715.7 ±10.5 -708.8 ±15.1 -719.0 ±3.3 -720.9 ±0.6 -663.5 ±18.3 6061-T6 -716.8 ±15.3 -750.7 ±4.8 -737.6 ±11.5 -739.6 ±3.5 -734.8 ±0.1 -689.7 ±4.4 C26N-PB -679.2 ±5.3 -695.6 ±0.7 -686.3 ±1.5 -692.3 ±1.0 -693.7 ±0.7 -640.1 ±5.0

147 Table 4.3: Corrosion current density, icorr, measured in quiescent solutions of G110 (1 M NaCl + H2O2), B117 (0.9 M NaCl), G85-A2 2+ - 2 (0.9 M NaCl + pH 3), CASS (0.9 M NaCl + Cu + pH 3), and GMW (0.2 M [Cl ] + NaHCO3). Unit: 10e-6 A/cm .

ASTM G110 ASTM B117 ASTM G85-A2 CASS GMW14872

6022-T4 1.1 ±0.3 0.4 ±0.2 4.4 ±0.1 23.0 ±1.4 0.5 ±0.2 6061-T4 12.8 ±9.8 0.5 ±0.3 4.3 ±0.1 28.1 ±0.3 2.0 ±1.6 6061-T6 37.1 ±7.6 0.6 ±0.2 4.5 ±0.7 17.9 ±1.3 1.6 ±0 .8 C26N-PB 7.4 ±0.7 0.3 ±0.3 1.7 ±0.1 11.1 ±1.3 0.7 ±0.4

Table 4.4: Density of Fe-rich IMPs measured on SEM images and the percentage of area occupied by IMPs.

Alloy Density 103 mm-1 % area

6022-T4 3.375 0.65 6061-T4 4.569 2.05 6061-T6 10.26 2.05

148

Table 4.5: Proposed corrosion pathways and corresponding alloys and test environment (including results from Chapter 2 and 3).

# Proposed Pathway Alloy Test environment ASTM G110/ ASTM B117/ Cyclic 1 Shallow pitting All B117/ GMW14872 2 Larger-scale pitting 6061-T4 ASTM B117 6061-T6, C26N- 3 IGC GMW14872 Fe-bearing IMPs PB Shallow pitting Larger-scale Grain pitting, Pit- 6061-T6, C26N- 4 IGC ASTM B117 dissolution associated PB IGC Larger-scale pitting, Pit-associated Field/ASTM G110/CASS/ASTM 5 6061-T6 IGC G85-A2 Microsegregation 6022-T4/PB Field/CASS/ASTM G85-A2 IGC Larger-scale pitting, 6 at GBs Field/ASTM G110/CASS/ASTM Grain Pit-associated IGC C26N-PB dissolution G85-A2 7 Larger-scale pitting 6061-T4 Field/CASS/ASTM G85-A2

149 L a)

S

b)

c)

Figure 4.1: Process for particle cluster analysis. a) Backscattered SEM image of the L x S surface. b) particle identified through brightness difference. c) Fit ellipses of IMPs.

150

a)

Al5FeSi

b)

Q-Al4Cu2Mg8Si7

Figure 4.2: Modeled solidification curves for a) Al5FeSi and b) Q-Al4Cu2Mg8Si7 using Thermo-Calc Software TCAL6: TCS Aluminum-based Alloys Database.

151

Connect to RE Solution

Platinum wire

Sealed with silicone rubber Capillary

Figure 4.3: Experiment setup for potentiodynamic polarization using electrochemical microcell.

152 a) Element Atomic % Al 68.63 Fe 24.21 Si 6.68

b) Element Atomic % Al 56.44 Cu 29.18 Mg 8.55 Si 5.84

Element Atomic % Al 15.97 Cu 9.72 Mg 42.12 Si 32.19

Figure 4.4: Backscattered SEM images of cast surrogates of a) Al5FeSi and b) Q-Al Cu Mg Si Atomic weight of elements is shown in the tables. 4 2 8 7.

Figure 4.5: Optical micrograph of cast surrogate of a) Al75(Fe13MnCr)Si10 and b) Q phase after anodic polarization with microcell. The exposed area is indicated by the red circle.

153

Figure 4.6: Backscattered SEM of the L X T surface of a) 6022-T4, b) 6061-T4, c) 6061-T6, and d) C26N-PB. Accelerating voltage 20 kV. Spot size 5. Scale bar:

400 µm.

154

a) b)

2 1 1 2 3 20 µm 50 µm

at. % Al Mg Si Fe Mn Cr at. % Al Mg Si Fe Mn Cr Spot 1 74.7 / 9.0 13.6 0.5 1.5 Spot 1 87.1 1.0 6.3 5.2 0.3 0.1 Spot 2 69.1 18.3 12.5 / / / Spot 2 90.0 / 6.5 3.5 / / Spot 3 98.4 1.1 0.4 / / / d) c)

2 2 1

3 1 100 µm 20 µm at. % Al Mg Si Fe Mn Cr at. % Al Mg Si Fe Mn Cr Spot 1 75.1 / 8.3 13.4 1.2 1.4 Spot 1 72.3 / 10.3 11.7 2.3 2.2 Spot 2 81.0 9.6 9.5 / / / Spot 3 61.4 22.4 16.2 / / / Spot 2 91.3 / 4.3 3.7 0.7 /

Figure 4.7: Magnified view of IMPs on the L x T surface of a) 6022-T4, b) 6061- T4, c) 6061-T6, and d) C26N-PB. Atomic weight of elements in the particles were determined by EDS and given in the table below the images. The accelerating voltage and spot size were adjusted to optimize the image quality.

155

Figure 4.8: Cumulative probability of the height of clusters in the short transverse direction of a) 6022-T4, b) 6061-T4, c) 6061-T6, and d) C26N-PB with different multiplication factor. The original fit ellipses were used for m = 1.

156

Figure 4.9: Relative probability (column) and cumulative probability (curve) of pit depth (a-b) and area (c-d) of 6022-T4 measured by OP after 2 days and 30 days of exposure in ASTM B117.

157

Figure 4.10: Relative probability (column) and cumulative probability (curve) of pit depth (a-b) and area (c-d) of 6061-T6 measured by OP after 2 days and 30 days of exposure in ASTM B117.

158

Figure 4.11: Relative probability (column) and cumulative probability (curve) of pit depth (a-b) and area (c-d) of C26N-PB measured by OP after 2 days and 30 days of exposure in ASTM B117.

159

Figure 4.12: Relative probability (column) and cumulative probability (curve) of pit depth (a-b) and area (c-d) of 6022-T4 measured by OP after 14 days and 54 days of exposure in GMW 14872.

160

Figure 4.13: Relative probability (column) and cumulative probability (curve) of pit depth (a-b) and area (c-d) of 6061-T6 measured by OP after 14 days and 54 days of exposure in GMW 14872.

161

Figure 4.14: Relative probability (column) and cumulative probability (curve) of pit depth (a-b) and area (c-d) of C26N-PB measured by OP after 14 days and 54 days of exposure in GMW 14872.

162 Figure 4.15: Potentiodynamic polarization curves in the corrosion test solutions and cyclic polarization curve in deaerated 5 wt.% (0.9 M) NaCl solution for 6022-T4. The corresponding corrosion test are ASTM B117 (0.9 M NaCl), ASTM G85-A2 (0.9 M 2+ NaCl + pH 3), CASS (0.9 M NaCl + Cu + pH 3), ASTM G110 (1 M NaCl + H2O2), and - GMW14872 (0.2 M [Cl ] + NaHCO3).

Figure 4.16: Potentiodynamic polarization curves in the corrosion test solutions and cyclic polarization curve in deaerated 5 wt.% (0.9 M) NaCl solution for C26N-PB. The corresponding corrosion test are ASTM B117 (0.9 M NaCl), ASTM G85-A2 (0.9 M 2+ NaCl + pH 3), CASS (0.9 M NaCl + Cu + pH 3), ASTM G110 (1 M NaCl + H2O2), - and GMW14872 (0.2 M [Cl ] + NaHCO3). 163 Figure 4.17: Potentiodynamic polarization curves in the corrosion test solutions and cyclic polarization curve in deaerated 5 wt.% (0.9 M) NaCl solution for 6061-T4. The corresponding corrosion test are ASTM B117 (0.9 M NaCl), ASTM G85-A2 (0.9 M 2+ NaCl + pH 3), CASS (0.9 M NaCl + Cu + pH 3), ASTM G110 (1 M NaCl + H2O2), and - GMW14872 (0.2 M [Cl ] + NaHCO3).

Figure 4.18: Potentiodynamic polarization curves in the corrosion test solutions and cyclic polarization curve in deaerated 5 wt.% (0.9 M) NaCl solution for 6061-T6. The corresponding corrosion test are ASTM B117 (0.9 M NaCl), ASTM G85-A2 (0.9 M 2+ NaCl + pH 3), CASS (0.9 M NaCl + Cu + pH 3), ASTM G110 (1 M NaCl + H2O2), - and GMW14872 (0.2 M [Cl ] + NaHCO3). 164

Figure 4.19: Representative potentiodynamic polarization curves measured on cast surrogates of second phases and pure Al in a) 5 wt.% NaCl, b) 5 wt.% NaCl at pH3, and c) mixed salt solution of 0.9 wt.% NaCl, 0.1 wt.% CaCl2, and 0.075 wt.% NaHCO3. 165

Figure 4.20: Plot of the measured corrosion potential and corrosion current density of cast surrogates of second phases and pure Al in a) 5 wt.% NaCl, b) 5 wt.% NaCl at pH=3, and c) mixed salt solution of 0.9 wt.% NaCl, 0.1 wt.% CaCl2, and 0.075 wt.% NaHCO . All values were taken from polarization curves measured using the microcell. 3 The standard deviation is indicated by the error bars. 166

Figure 4.21: Optical micrograph of cross-section of 6061-T6’ after exposure in a) ASTM G110 for 24 hours, b) ASTM B117 for 30 days, c) ASTM G85-A2 for 30 days, d) CASS for 44 hours.

Figure 4.22: Cumulative probability of pit depth in ASTM B117 and cluster height with certain throwing power, m. Black lines are depths of shallow pits after 30 days of ASTM B117 measured by OP. Red lines are cluster height with certain throwing power for corresponding alloys.

167

Figure 4.23: Schematic of formation of particle clusters depending on particle density and throwing power.

168

Figure 4.24: Cumulative probability of the area of IMPs and pit area after ASTM B117 (30 days) and GMW14872 (54 cycles) of a) 6022-T4 and b) 6061-T6.

169

Figure 4.25: Schematic of relationship between microstructural features and corrosion types.

170 5. Conclusions and Impact

5.1 Conclusions

In this research, the corrosion behavior of commercial Al-Mg-Si alloys, AA6022-

T4/PB, AA6061-T4/T6, and C26N-PB, after field exposure was investigated and compared with that after several typical laboratory corrosion tests. Corrosion of Al-Mg-Si alloys in field and laboratory corrosion tests usually exhibited complex morphologies and varied depending on corrosion condition and alloy type.

Corrosion attack after field exposure was highly localized across all alloy types.

While most of the surface area remained unattacked after 2 years, the localized attack was observed in as short as 3 months of field exposure, which was before the first peak winter. IGC and intra-granular corrosion associated with IGC were the most commonly seen corrosion morphologies after field exposure. For all the alloys except for 6061-T4,

IGC generally dominated. For 6061-T4, intra-granular corrosion was preferred and only wisps of IGC were observed in the attacked region. This dominant intragranular corrosion in 6061-T4 was likely due to higher Cu content but low susceptibility to IGC because of the lack of artificial aging and excessive Si at grain boundaries. Shallow pits caused by trenching around IMPs were seen in a limited number of cases but did not dominate the corrosion morphology during field exposure.

171 Quantitative comparison of corrosion morphology using fractal analysis and L2/A suggested that none of the laboratory tests investigated in this study led to the exact same corrosion morphology to that in field. Yet, CASS and ASTM G85-A2, which utilized acidified NaCl solutions, generally exhibited better correlation with field exposure across all alloy types. For 6022-T4, although a similar morphology was identified by visual examination, IGC and irregular boundaries due to intra-granular corrosion were more commonly seen in field exposure than in CASS and ASTM G85-A2 as indicated by more corrosion sites in field exposure with high L2/A and/or FD. For 6061-T4, CASS outperformed the other tests that also caused larger-scale pits as indicated by the FD and

L2/A parameters. In addition to CASS and ASTM G85-A2, ASTM G110 well correlated to field exposure per the FD and L2/A parameters for 6061-T6 alone. The results from image classification using GoogleNet are generally in good agreement with visual observation.

Based on visual and quantitative examination of corrosion morphology, the pH of solutions used in accelerated tests is the critical environmental variable that controls corrosion morphology. In laboratory tests using pH neutral solutions, shallow pitting induced by trenching around cathodic particles (generally with a depth <10 µm) was the most commonly observed corrosion morphology. Larger-scale pitting and pitting- associated IGC dominated on all alloys in salt spray tests that use acidified NaCl solutions (around pH 3). This attack likely developed from isolated IGC followed by grain dissolution or grain fallout depending on the susceptibility to IGC. The overall corrosion morphology after field exposure was likely determined by a synergetic process

172 of IGC and intra-granular corrosion. Extensive IGC was obtained in the alloys with a low

Mg/Si ratio (6022-T4) or relative high amount of Cu with artificial aging (6061-T6).

Optical profilometry and distribution analysis of IMPs revealed that an individual cathodic IMP may not able to cause a stable pit because the trench around the particle cannot grow exceeding the critical depth before the removal of the particle. Possibility for the formation of stable pits increases with higher particle density and throwing power due to particle clustering.

5.2 Technological Impacts

With regard to life-time estimation of Al alloys in an automotive environment, cautions should be taken when an accelerated test is selected or designed. Different corrosion mechanisms could be favored depending on the environment. For Al-Mg-Si alloys, an acidified NaCl solution is recommended for accelerated salt spray tests in order to better simulate the corrosion morphology in field exposure. Addition of wet/dry cycles to an accelerated test may not be necessary if an acidified solution is used.

For engineering perspective, fractal analysis and L2/A were able to distinguish different corrosion morphologies and provide a quantitative way to compare complex morphologies across all samples. The machine learning method provided an alternative way to compare the similarity of corrosion morphology in different environment, but it requires a relatively large data set. Quantitative parameters can be used to develop new accelerated tests to simulate the corrosion morphology in the Field.

173 6. Future Work

This research investigated the ability of standard accelerated corrosion tests to simulate the corrosion morphology of Al-Mg-Si alloys exposed to automotive service environments and provided an understanding on the effects of microstructural heterogeneities on the corrosion behavior of these alloys. However, there are still issues that need to be investigated in the future:

1. Effect of relative humidity (RH) on corrosion morphology

Due to the limitation of the ability of the salt spray chamber, tight control of RH

was not possible during laboratory corrosion testing. During the drying stage, RH

can play an important role on the time of wetness on the exposed coupon and

affect corrosion morphology. It was found in this work that, by introducing a

drying stage (cyclic B117 and GMW 14872), pure IGC developed in 6061-T6

instead of larger-pit-associated IGC. It is not clear whether the wet/dry cycle

hinders dissolution of grains or if the residual electrolyte that is enriched with

chloride ions, enhances initiation of IGC. Further investigation into the effect of

RH on corrosion morphology would be beneficial for the development of

accelerated tests.

2. IGC and pit growth mechanisms

174 Pit-associated IGC was commonly seen during both field exposure and

accelerated tests. However, the interaction between pit growth and IGC could not

be well explained by the results from exposure. During field exposure, IGC was

more dominant as indicated by the higher L2/A ratio in 6022-T4, while only wisps

of IGC were observed around larger-scale pits after CASS and ASTM G85-A2. In

addition, for 6061-T6, large scale pitting may be a cavity left behind after grain

fallout from IGC. This understanding could be important to improve the ability of

CASS and ASTM G85-A2 to simulate corrosion morphology in the field.

3. Corrosion morphology recognition with convolutional neutral network (CNN)

The CNN method in this work provided an alternative way to evaluate similarity

between cross-sections of localized attack. A large size of training data set could

improve the accuracy of classification. In addition, further investigation is

required to identify the features that the CNN model is used for classification.

This understanding could be important for the validation of the results from CNN

classification.

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183 Appendix A. Matlab Codes

% Image processing and length/area ratios clear file = dir('*.png'); Num = size(file); Data = []; mkdir BW mkdir Edge rsize = 0.5; mkdir Resized0.5 for i = 1:Num(1) %Total Area name = file(i).name; splitname = split(name,'.'); dim = split(splitname(1)); width = str2num(cell2mat(dim(1))); height = str2num(cell2mat(dim(2))); totalarea = width * height;

%Image processing fig = imread(name); s = size(fig); s = size(s); if s(2) == 3 fig = rgb2gray(fig); end % fig = adapthisteq(fig);

%adjust threshold [counts,x] = imhist(fig,256); T = otsuthresh(counts); BW = imbinarize(fig,T); BW = imcomplement(BW); BW = bwareaopen(BW,4); BW = imcomplement(BW);

184 cd BW BWname = string(width) + ' ' + string(height) + ' ' + 'BW' + '.png'; imwrite(BW,BWname) cd .. cd Edge edgeim = edge(BW,'canny'); % I = imfill(edgeim,'holes'); Ename = string(width) + ' ' + string(height) + ' ' + 'Edge' + '.png'; imwrite(edgeim,Ename) cd ..

%Resize image to 0.5 um per pixel pixsize = size(BW); originpixsize = width/pixsize(2); resizefactor = originpixsize/rsize; resizedBW = imresize(BW,resizefactor); resizedEdge = edge(resizedBW,'canny'); cd Resized0.5 Ename = string(width) + ' ' + string(height) + ' ' + string(round(originpixsize,2)) + '.png'; imwrite(resizedEdge,Ename) cd ..

%Corroded Area ratio [N,edges] = histcounts(resizedBW); Areafraction = N(1)/(N(1)+N(2)); CorrArea = Areafraction * totalarea;

%Edge ratio = edge curve length/corroded area % [Nedge,edges] = histcounts(resizedEdge); % EdgeRatio = Nedge(2)/N(1); resizedsize = size(resizedEdge); diag = 0; rbsize = 0;

for ri = 2:resizedsize(1)-1 for rj = 2:resizedsize(2)-1 if resizedEdge(ri,rj) == 1 rbsize = rbsize + 1; conn4 = resizedEdge(ri+1,rj)+resizedEdge(ri-1,rj)+... resizedEdge(ri,rj+1)+resizedEdge(ri,rj- 1); if conn4 == 1 185 diag = diag+0.5;

end if conn4 == 0 diag = diag+1;

end end end end

rblength = rbsize * rsize + (sqrt(2)-1)*diag * rsize; EdgeRatio = rblength/CorrArea; Edge2Ratio = rblength^2/CorrArea;

%Data output Data(i,1) = height; Data(i,2) = width; Data(i,3) = CorrArea; Data(i,4) = EdgeRatio; Data(i,5) = Edge2Ratio; % Data(i,6) = rblength; end csvwrite('EdgeRatio0.5.csv', Data) fractal

% Fractal dimension using box-counting method

clear cd Edge file = dir('*.png'); Num = size(file); Data = []; Dataall = []; fitdata = []; fitdataall = []; for i = 1:Num(1) %real dimension 186 name = file(i).name; splitname = split(name,'.'); dim = split(splitname(1)); width = str2num(cell2mat(dim(1))); height = str2num(cell2mat(dim(2)));

fig = imread(name); [n,r]=boxcount(fig);

%convert to real dimension %length of one pixel,a totalpixel = size(fig); a = sqrt(width*height/(totalpixel(1)*totalpixel(2))); r=r*a;

%save to data r = r'; n = n'; x = log(r); y = log(n); temp = horzcat(r,n); stepn = size(temp); for m = 1:stepn(1) Data(m,i*2-1) = temp(m,1); Data(m,i*2) = temp(m,2); end [fitobject,gof] = fit(x,y,'poly1','Exclude', x < 0); slope = -fitobject.p1; inter = fitobject.p2; rsq = gof.rsquare; fitresult = horzcat(slope,inter,rsq); fitdata = vertcat(fitdata,fitresult);

[fitobject,gof] = fit(x,y,'poly1'); slope = -fitobject.p1; inter = fitobject.p2; rsq = gof.rsquare; fitresultall = horzcat(slope,inter,rsq); fitdataall = vertcat(fitdataall,fitresultall);

end figure hold on for i = 1:Num(1) 187 loglog(Data(:,2*i-1),Data(:,2*i),'.-') end hold off set(gca, 'XScale', 'log', 'YScale', 'log'); xlabel('log(r) log(\mum)') ylabel('log(N)') saveas(gcf,'fractal.png') close cd .. csvwrite('fractal.csv', Data) csvwrite('fitdata.csv', fitdata) csvwrite('fitdataall.csv', fitdataall) function [n,r] = boxcount(c,varargin)

% control input argument error(nargchk(1,2,nargin));

% check for true color image (m-by-n-by-3 array) if ndims(c)==3 if size(c,3)==3 && size(c,1)>=8 && size(c,2)>=8 c = sum(c,3); end end warning off c = logical(squeeze(c)); warning on dim = ndims(c); % dim is 2 for a vector or a matrix, 3 for a cube if dim>3 error('Maximum dimension is 3.'); end

% transpose the vector to a 1-by-n vector if length(c)==numel(c) dim=1; if size(c,1)~=1 c = c'; end end width = max(size(c)); % largest size of the box p = log(width)/log(2); % nbre of generations 188

% remap the array if the sizes are not all equal, % or if they are not power of two % (this slows down the computation!) if p~=round(p) || any(size(c)~=width) p = ceil(p); width = 2^p; switch dim case 1 mz = zeros(1,width); mz(1:length(c)) = c; c = mz; case 2 mz = zeros(width, width); mz(1:size(c,1), 1:size(c,2)) = c; c = mz; case 3 mz = zeros(width, width, width); mz(1:size(c,1), 1:size(c,2), 1:size(c,3)) = c; c = mz; end end n=zeros(1,p+1); % pre-allocate the number of box of size r switch dim

case 1 %------1D boxcount ------%

n(p+1) = sum(c); for g=(p-1):-1:0 siz = 2^(p-g); siz2 = round(siz/2); for i=1:siz:(width-siz+1) c(i) = ( c(i) || c(i+siz2)); end n(g+1) = sum(c(1:siz:(width-siz+1))); end

case 2 %------2D boxcount ------%

n(p+1) = sum(c(:)); for g=(p-1):-1:0 siz = 2^(p-g); 189 siz2 = round(siz/2); d = []; for i=1:siz:(width-siz+1) for j=1:siz:(width-siz+1) for w=0:(siz-1) for h=0:(siz-1) c(i,j)=(c(i,j)||c(i+w,j+h)); end end c(i,j) = ( c(i,j) || c(i+siz2,j) || c(i,j+siz2) || c(i+siz2,j+siz2) ); % if i==1 && j == 1 % d(1,1) = c(i,j); % else % d(1+(i-1)/siz,1+(j-1)/siz) = c(i,j); % end end end % figure % imshow(d)

n(g+1) = sum(sum(c(1:siz:(width- siz+1),1:siz:(width-siz+1)))); end

case 3 %------3D boxcount ------%

n(p+1) = sum(c(:)); for g=(p-1):-1:0 siz = 2^(p-g); siz2 = round(siz/2); for i=1:siz:(width-siz+1), for j=1:siz:(width-siz+1), for k=1:siz:(width-siz+1), c(i,j,k)=( c(i,j,k) || c(i+siz2,j,k) || c(i,j+siz2,k) ... || c(i+siz2,j+siz2,k) || c(i,j,k+siz2) || c(i+siz2,j,k+siz2) ... || c(i,j+siz2,k+siz2) || c(i+siz2,j+siz2,k+siz2)); end end end

190 n(g+1) = sum(sum(sum(c(1:siz:(width- siz+1),1:siz:(width-siz+1),1:siz:(width-siz+1))))); end end n = n(end:-1:1); r = 2.^(0:p); % box size (1, 2, 4, 8...) if any(strncmpi(varargin,'slope',1)) s=-gradient(log(n))./gradient(log(r)); semilogx(r, s, 's-'); ylim([0 dim]); xlabel('r, box size'); ylabel('- d ln n / d ln r, local dimension'); title([num2str(dim) 'D box-count']); elseif nargout==0 || any(strncmpi(varargin,'plot',1)) loglog(r,n,'s-'); xlabel('r, box size'); ylabel('n(r), number of boxes'); title([num2str(dim) 'D box-count']); end if nargout==0 clear r n end end

%image processing for transfer learning clear

%read image filename = 'Data/CASS'; ds = ['processed' filename 'sub']; mkdir(ds) imds = imageDatastore(filename); num = size(imds.Files);

%get image size net=googlenet; inputSize = net.Layers(1).InputSize; for i = 1:num

191 img = readimage(imds,i); imgsize = size(img); dimension = size(imgsize);

if dimension(2) == 2 %gray to index to RGB img = imadjust(img); img = gray2ind(img);

cmap = colormap(gray);

img = ind2rgb(img,cmap);

img = imadjust(img,[],[],0.5); end

nx = ceil(imgsize(2)/inputSize(2)); y1 = imgsize(1)/2-inputSize(1)/2; x1 = 0; increment = floor((imgsize(2)-inputSize(2))/(nx-1));

%extract center sections for j = 1:nx J = imcrop(img,[x1 y1 inputSize(2)-1 inputSize(1)- 1]); name = [ds '/' int2str(i) int2str(j) '.tif']; if imagecheck(J) imwrite(J,name,'tif') end x1 = x1 + increment; end % extract underlying sections y1 = y1 + inputSize(1); x1 = 0; for j = 1:nx J = imcrop(img,[x1 y1 inputSize(2)-1 inputSize(1)- 1]); name = [ds '/' int2str(i) int2str(j) 'sub' '.tif']; if imagecheck(J) imwrite(J,name,'tif') end

x1 = x1 + increment; end end

192

%Transfer training with Googlenet clear %read image filename = 'ProcessedData'; imds = imageDatastore(filename,'IncludeSubfolders',true,'LabelSour ce','foldernames');

[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7);

%Train with new images net=googlenet; inputSize = net.Layers(1).InputSize; if isa(net,'SeriesNetwork') lgraph = layerGraph(net.Layers); else lgraph = layerGraph(net); end

[learnableLayer,classLayer] = findLayersToReplace(lgraph); numClasses = numel(categories(imdsTrain.Labels)); if isa(learnableLayer,'nnet.cnn.layer.FullyConnectedLayer') newLearnableLayer = fullyConnectedLayer(numClasses, ... 'Name','new_fc', ... 'WeightLearnRateFactor',10, ... 'BiasLearnRateFactor',10); elseif isa(learnableLayer,'nnet.cnn.layer.Convolution2DLayer') newLearnableLayer = convolution2dLayer(1,numClasses, ... 'Name','new_conv', ... 'WeightLearnRateFactor',10, ... 'BiasLearnRateFactor',10); end lgraph = replaceLayer(lgraph,learnableLayer.Name,newLearnableLayer);

193 newClassLayer = classificationLayer('Name','new_classoutput'); lgraph = replaceLayer(lgraph,classLayer.Name,newClassLayer); layers = lgraph.Layers; connections = lgraph.Connections; layers(1:10) = freezeWeights(layers(1:10)); lgraph = createLgraphUsingConnections(layers,connections);

%Generate augmented images for training aug = imageDataAugmenter('RandXReflection',true, 'RandYReflection',true,... 'RandRotation',[0 360],'Randscale',[0.5 1]); augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain,... 'DataAugmentation',aug,... 'ColorPreprocessing','gray2rgb'); augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation,... 'ColorPreprocessing','gray2rgb'); % %check % minibatch = read(augimdsValidation); % imshow(imtile(minibatch.input)) miniBatchSize = 20; valFrequency = floor(numel(augimdsTrain.Files)/miniBatchSize); options = trainingOptions('sgdm', ... 'MiniBatchSize',miniBatchSize, ... 'MaxEpochs',10, ... 'InitialLearnRate',3e-4, ... 'Shuffle','every-epoch', ... 'ValidationData',augimdsValidation, ... 'ValidationFrequency',valFrequency, ... 'Verbose',false, ... 'Plots','training-progress',... 'ExecutionEnvironment','multi-gpu'); net = trainNetwork(augimdsTrain,lgraph,options);

[YPred,probs] = classify(net,augimdsValidation); 194 accuracy = mean(YPred == imdsValidation.Labels) idx = randperm(numel(imdsValidation.Files),10); figure for i = 1:10 subplot(2,5,i) I = readimage(imdsValidation,idx(i)); imshow(I) real = imdsValidation.Labels(idx(i)); label = [YPred(idx(i)) 'real' real]; title(string(label) + ", " + num2str(100*max(probs(idx(i),:)),3) + "%"); end trainedgoogle_balanced = net; save trainedgoogle_balanced

%plot confusion matrix figure('Units','normalized','Position',[0.2 0.2 0.4 0.4]); cm = confusionchart(imdsValidation.Labels,YPred); cm.Title = 'Confusion Matrix for Validation Data'; cm.ColumnSummary = 'column-normalized'; cm.RowSummary = 'row-normalized';

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