LONG-TERM PITTING CORROSION OF 6060 ALUMINIUM ALLOY IMMERSED IN NATURAL SEAWATER
M. X. Liang, I. A. Chaves, R. E. Melchers Centre for Infrastructure Performance and Reliability, The University of Newcastle, NSW2308, Australia
SUMMARY: Aluminium alloys are widely used in maritime industries because of their high strength to weight ratio, ease of fabrication and expected corrosion resistance. However, they are susceptible to localised corrosion under specific corrosive environment. Further, information on the long-term corrosion characteristics of aluminium alloys under natural seawater immersed condition is scarce. Hence, this study reports a field investigation on pitting corrosion data of 6060 aluminium alloy immersed for two years in natural seawater with average annual temperature of 20oC. An Optical Microscope was used to examine pit morphology and to measure pit depths. Cross-section microstructure and chemical composition of pits were investigated by means of Scanning Electron Microscopy and Energy Dispersive Spectrometry. Five deepest pits were measured on each face of a sextuplicate set of coupons. The pit depth data was analysed using extreme value statistics. Results show that the depth of the deepest pits progressed in a ‘step-wise’ manner. Pitting severity and the maximum pit depth increased with the depth of immersion. The results support previous findings indicating changes in corrosion mechanism with time. Similar to the corrosion of steels, this is considered to result from the build-up of corrosion products. The reason for this is discussed and further work is outlined.
Keywords: Aluminium alloys; pitting corrosion; seawater immersion; microstructure.
1. INTRODUCTION Aluminium alloys are widely used in various fields such as marine infrastructures and aerospace due to their high strength to weight ratio and good corrosion resistance (Perryman 2007, Srinivasa Rao 2004). In recent years, the application of aluminium alloys in structures is increasing, and they became the second prevalent metal alloys used in industry (Vargel 2004). In general, aluminium alloys have both good general corrosion and pitting corrosion resistance in atmospheric environments (de la Fuente et al. 2007). However, they tend to suffer localised corrosion when exposed to the aggressive aqueous environment, such as seawater immersion, for an extended length of time.
Pitting corrosion is the most common form of corrosion for aluminium and its alloys (Vargel 2004). It is a long- term hazard for the integrity, safety and serviceability of both new and existing infrastructures (Chaves and Melchers 2014). Pitting corrosion may cause perforation thus pose a potential threat of fracture and other damage modes (Melchers 2015). Up to now, a number of studies have reported on the corrosion of aluminium alloys, such as laboratory-based experiments with simulated seawater as exposure medium (Szklarska- Smialowska 1986) and mathematical based probabilistic modelling for the growth of corrosion pits (Harlow and Wei 1998). Nevertheless, most report the corrosion behaviour of aluminium within a “short-term” (seconds and hours) or “middle-term” (days and weeks) exposure period. This may be somewhat irrelevant to “long-term” (years and decades) practical design considerations. Only limited empirical investigations have considered long- term corrosion loss measurements, for instance, mass loss and pit depth (Southwell et al. 1964, Ailor 1974, Bopinder et al. 1997). It follows that long-term corrosion progressions with time and corresponding mechanisms
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remain unclear. Moreover, when considering the reliability based design of aluminium structures, it is of great importance to have the ability to reliably predict the long-term development of pitting corrosion behaviour (Chaves and Melchers 2014).
One way to predict long-term maximum pitting depth with time is to use the most widely-applied power-law function (Szklarska. Smialowska 1986, Vargel 2004): c(t) =At (1) where c is pit depth, t is exposure time and A and B are constants obtained from fitting the function to data. The values of A and B vary and depend on the nature of alloy, temperature and water velocity, etc. (Vargel 2004). Recent studies on corrosion of steels and aluminium alloys have found that the power-law equation is not the best fit for data from long-term exposures in the marine environment (Melchers 2006, Melchers 2010). Instead, a bi-modal model or multi-phase model has been proposed to give a better description for long-term corrosion data (Fig.1) (Melchers 2014b). Previous research has shown that the data for long-term corrosion of mild steels, irrespective of seawater immersion, tidal or marine atmospheric environment, consistently comply with a bi- model trend (Melchers 2014b, Melchers 2008). Most recently, the long-term corrosion data for aluminium pit depth and mass loss also have been shown to follow bi-model trends (Melchers 2014a, Melchers 2015).
Figure 1 Schematic bi-modal model for long-term corrosion loss and pit depth in marine environments (Melchers 2014a)
The theory underpinning the model is as follows: as pitting increases, the build-up corrosion products increase. The topographically non-uniformity of corrosion products facilitates the formation of anoxic conditions in the pits, which, as a result, promote the transformation of corrosion mechanism from corrosion rate controlled by oxygen reduction (mode 1 in Fig.1) to hydrogen reduction dominated (mode 2 in Fig.1) (Melchers 2014b).
Due to the lack of suitable long-term field data and insufficient understanding of the long-term corrosion progressions, especially associated with the point t in Fig.1, this paper reports data for long-term pitting corrosion of 6060 aluminium exposed in natural seawater for two years. Pits morphology and microstructure are discussed and the maximum depths of pits are analysed by applying extreme value statistics.
2. EXPERIMENTAL DETAILS AND PROCEDURES
2.1 Test site The natural seawater test site for the experimental work reported herein is at the NSW Fisheries Research Station at Taylors Beach, Port Stephens, NSW, Australia (32.33S, 151.03E). The parameters measured at this site have been reported before (Jeffrey and Melchers 2002). Located 17 km from the open sea the inlet continues a further 2 km inland and disperses onto a region of mangrove covered mud flats. The seawater characteristics at the test site are summarised in Table 1.
Table 1. Exposure and nutrient conditions at Taylors Beach test site. Data from Jeffery and Melchers (2002).
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acteristic Value
Minimum water depth, m 1.2
Maximum tidal movement, m 2.0
Water temperature, ℃ 10.4-30.2
Exposure Water velocity (peak), m/s 0.06
Dissolved oxygen, % saturation 95-100
PH 8.1-8.2
Salinity, % 2.01-3.48
Nitrates, mg/L as N 0.02
Nitrites, mg/L as N <0.01
Ammonia, mg/L as N 0.03
Nutrient Sulphate, mg/L 1700
Ortho phosphorous, Mg/L as P <0.005
Total phosphorous, mg/L as P 0.022
Calcium, mg/L 419
It is important to note that the temperature of the water varies during the day and in different seasons. Nonetheless, since all the test specimen strips are located very close to one another and under the same condition, there is a very low possibility that the difference of corrosion behaviour between specimens is a result of temperature changes. Thus only annual average temperature (around 20℃) is considered in this report.
Also, the water velocity at Taylors Beach is slow. It was reported that the peak velocity at the surface was around 0.05m/s and it would reach about 0.06m/s as the water depth increases by 0.5 ~ 1.0 meter (Jeffrey and Melchers 2002). A previous study has shown that at low water velocity (<1.0 m/s) the water flow effects corrosion only for a short period after first exposure (typically 30 days) (Melchers and Jeffrey 2004). Moreover, it has little direct further influence on the long-term corrosion rate of metal since the corrosion products and marine growth form on the surface of the metal and provide protection against the impingement of water. Hence in this report, it is assumed that the water velocity has no effects on the pitting corrosion of aluminium alloys.
2.2 Test materials The test materials were commercial Al 6060 extrusions at T5-temper state. The chemical composition is summarised in Table 2. A total of six specimen strips (750mm × 40mm × 6mm) were exposed parallel to the water surface. Four strips (strip A, strip B, strip C and strip D) were immersed 640mm under the low tide water level and two other strips (strip E and strip F) were immersed 160mm deep below the low tide water level. These are nominal immersion depths. Some strips were parallel to each other and others were perpendicular to these, as shown in Fig. 2. This was done to assess the effect of orientation on corrosion losses.
Table 2. Chemical composition of testing materials (weight %)
Si Fe Cu Mn Mg Pb Ni Zn Ti Sn Cr Al
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0.44 0.18 0.01 0.06 0.4 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 Balance
All aluminium strips were retrieved after two years’ exposure. Following standardised procedure (ASTM 2011a), the strips were cleaned by mechanically scraping loose debris off the surface, cleaning using nylon cloths under flowing tap water followed by ultrasonic cleaning and then air drying. Given the soft nature of aluminium, care was taken to avoid damage to the surface during all stages of cleaning. Once cleaned each strip was cut into six 60mm × 40mm × 6mm coupons, which were marked with indentations for identification.
Figure 2 Schematic view of the arrangement of test specimens.
2.3 Pit depth measurement After an initial visual inspection of pit morphology, the coupons were observed under an upright white light optical microscope (ZEISS AXIO Imager A1). The microstructure of the aluminium alloy, such as grain boundary, was investigated after metallographic preparation (ASTM 2011b): Coupons were first surface ground using silicon carbide papers in the order of 320grit, 800grit, 1000grit and then coarsely polished with 6μm diamond suspension and finally finely polished with 1μm abrasive. Coupons were then etched by Flick’s reagent (Weidmann and Guesnier 2016) in order to visualise the grain boundary. Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) were utilised to investigate pit cross-section microstructure and chemical composition of typical deep pits.
Pit depths were measured using the change in focal distance between the bottom and surrounding surface of a pit using an upright white light optical microscope (Zeiss AXIO Imager A1). This method is particularly suitable for measure of the small pits with narrow opening which are difficult to be measured by a gauge probe type instrument (ASTM 2013). Depth sensitivity was of 0.1 μm. To avoid bias edge effects, only the pits located in the central area (10mm square from each edge) of the coupon were measured. Some 50-60 pits were measured, on both sides of each coupon, to obtain the 5 deepest readings per coupon. The recorded 5 deepest pit values, per coupon, were each obtained from an average of three consecutive readings for the same pit.
3. RESULTS
3.1 Surface morphology As noted, sextuplicate coupons were produced from each strip. Fig. 3 shows the surface morphology of the aluminium strips exposed two years in natural seawater. Both the front face and the back face of the coupons were inspected. Compared to Fig. 2, the darkening appearance of the coupons (Fig. 3) is due to the intrinsic nature of the oxide film on the surface of aluminium (de la Fuente et al. 2007). Moreover, the outmost layer of the oxide film formed in natural water is amorphous and porous which facilitates the adsorption of bicarbonate and other fouling in the seawater. It can be eliminated in acid media (Vargel 2004) to give the coupons a silver- white like finish appearance, but then some of the base metal would have been lost and further pitting would
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have taken place, biasing the results. This probably explains why the surfaces covered by marine growth do not blacken.
Marine growth was evident on the entire surface of specimens, despite claims that biofouling has difficulty adhering to aluminium (Szklarska. Smialowska 1986). It is also known, however, that when conditions are suitable, marine fouling may develop very quickly on the surface of aluminium alloy, including barnacles, corals algae, sponges, etc. This is because aluminium (excluding copper-bearing aluminium alloys) has no antifouling effect, since its corrosion products such as Al(OH) are not toxic to marine organisms (Vargel 2004).
Figure 3 Morphology of 6060 aluminium strips exposed for two years in natural seawater: (a) front face of coupons; (b) back face of coupons
The effects of marine growth on the surface of aluminium can be adverse. On one hand, when the barnacles or other mollusc attach to the surface, they acidify the local medium and this results in brilliant and highly visible superficial pickling (Fig. 3 (a)). On the other hand, the biofouling appeared to have tightly covered the surface and to have protected it against contaminates suspending in the seawater and perhaps further corrosion (see Fig. 4(a) centre).
By comparison, the traces left by marine growth on the strips located 160mm below the low tide level were fewer and smaller than on the four strips located at 640mm below low tide level. This may be the result of different marine growth behaviours, which in turn could be affected by seawater depth, temperature, salinity, solar radiation and concentration of dissolved oxygen as these are known to vary gradually (Vargel 2004, Aguilera et al. 1999, Jeffrey and Melchers 2009). As a result, marine organism types and their quantity may vary as well.
The two images in Fig. 4 are considered representative of all of the coupons from corresponding depths. Little or no difference was observed between strips perpendicular to the direction of water flow. However, the surface of the coupon immersed deeper (640mm) in seawater corroded more severely than the coupons just under the low tide surface seawater level (160mm). In Fig. 4 (a), the number of pits is greater overall, presenting a higher pit concentration density, but also showing comparatively uniform distribution on the surface compared to coupons immersed 160mm below low water level.
Some small pits were observed that appeared to have coalesced to form bigger pits (shown by yellow arrows in Fig. 4 (a)). Moreover, in the centre area, an ’oval’ shaped ring can be observed. This is almost free from pitting corrosion. Since the area is much larger compared to the average grain size of 6060 aluminium alloy shown in Fig. 5, it is unlikely to be a phenomenon introduced by the material itself. However, it is possibly the result of differential aeration around the circumference of a marine organism (e.g. a larger barnacle) that also provides shielding from oxygen diffusion towards the inner area.
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Figure 4 Typical morphology of corroded surfaces of 6060 aluminium alloy after two years’ exposure: (a) surface of coupon immersed at 640mm depth; (b) surface of coupon immersed at 160mm depth. Scale bar=200micros.
In agreement with previous reports (Jeffrey and Melchers 2009), samples immersed 160mm below low tide level (Fig. 4 (b)) presented less corrosion damage compared with strips at 640mm below the low tide water level. In most case, a substantial portion of the strip surface remained unattacked after two years. Where corrosion damage was observed, the pits formed were shallower and wider compared to the deep and narrow pits observed on coupons immersed at the 640mm below low tide level. It is possible that the cluster of pits (centre of Fig.4 b) was caused by material imperfection, such as damage of oxide film, intermetallic particles, etc. as proposed by Szklarska-Smialowska (1999).
Figure 5 Microstructure of 6060 aluminium alloy. Scale bar=200microns.
Further, very deep pits were observed on the surfaces of all coupons. Their depth and diameter were significantly larger than those of other, surrounding, pits. Fig. 6(a) and Fig. 6(b) show examples of such pitting, on coupons immersed 640mm and 160mm below low tide water level respectively. (Note the locations of the pits in Fig. 6(a) and Fig. 6(b) are marked by yellow arrows in Fig. 3).
The pits in Fig. 6 (a) observed on coupons at some 640mm immersion depth have extremely large pit openings (approximately 4 mm in nominal diameter) and depth (2.023mm in depth). By comparison, the extreme pit shown in Fig. 6 (b) observed on a coupon immersed at 160mm depth is significantly smaller (1.5mm in diameter) and shallower (0.472mm in depth), but still much larger than the surrounding pits. Also, the shapes of the pit openings in Fig. 6 are non-regular, and the topography of pitting corroding surface, showing obvious ‘step-wise’ character as marked by red arrows, is quite non-uniform. Such ‘step-wise’ pattern suggests that a complex corrosion process may have taken place over time The reasons for this phenomenon and the potential implications are discussed in section 4.
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Figure 6 Morphology of pits with extreme depth: (a) on coupon located 640mm below low tide water level; (b) on coupon located 160mm under low tide water level. Scale bar=1mm.
3.2 Statistical analysis of maximum pit depth It is a common practice to apply extreme value analysis to maximum pit depths to determine whether an extreme distribution is an appropriate model for the uncertainty associated with pit depth (Chaves and Melchers 2014, Melchers 2008). Conventionally this is achieved by verifying if the scatter in the data for pit depths is appropriately represented by an extreme value distribution. By ranking the maximum observed pit depths and assigning each a rank order of occurrence, their corresponding frequency can be obtained (Ang and Tang 2007). In order to better visualise the data’s distribution frequency the so measured pit depth data is then plotted along the horizontal axis and the associated rank order frequencies are then plotted along the vertical axis with the probability represented by the standardised normal variate (Galambos 1987). The standardised variable W is defined as =( − ) , further defined through ( ) = [( − ) ] with ( ) =exp − and
( ) = [( − ) ]. Here and , respectively, are the “mode” and “slope” of the straight line fitted to the data on the Gumbel plot, as shown in Fig. 7 (a). They are related to the mean and standard deviation of the Gumbel distribution through the following expressions: = + 0.577216/ and = /√6 (Ang and Tang 2007). In turn, by confirming that the plotted data fits a linear function on a Gumbel plot this technique can indicate whether the Gumbel function is an appropriate choice for representing the data (Chaves and Melchers 2011, Chaves and Melchers 2014).
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Figure 7 Gumbel probability plot showing two sets of data for maximum pit depth for 6060 aluminium coupons immersed for two years in natural seawater (annual average temperature of 20℃): (a) with Gumbel trend lines best-fitted to the data; (b) same data with interpreted trend showing non-linear characteristic.
The data used to generate the frequency plot shown in Fig. 7 consists of the collection of the five deepest pit depths measured for coupon face. Given that each strip was comprised of 6 coupons, and there were 4 strips (strips A, B, C and D) retrieved from the lower immersion depth and 2 strips (strips E and D) from the higher immersion depth, the set of extreme pit depth data consist of 120 and 60 measurements respectively. Although in principle a small corrosion allowance should be added to the relative pit depth measurements described above, this has been ignored here since aluminium has good resistance to general corrosion and thus the correction would be slight (Szklarska-Smialowska 1986). It follows that the relative pit depth is considered here to be equal to absolute pit depth.
It is clear that the data in Fig. 7 shows considerable scatter. When each data set is provided with a best-fit straight line (Fig. 7 (a)) it is seen that in each case the data sets are inconsistent with a single straight line. This indicates that the Gumbel probability distribution is not a good fit to the data. In turn, this means that the Gumbel extreme value function is not appropriate for representing the variability for long-term maximum pit depth of aluminium in a seawater immersion environment.
Instead of a linear trend, the extreme pit depth data obtained from strips immersed at 640mm depth can be seen to display a non-linear trend, as shown in Fig. 7 (b). Similar non-linear trends have been found for other cases of extreme pit depths ( Melchers 2014b, Melchers 2015). With a little interpretation, a similar non-linear trend can be observed also for the data for coupons at160mm depth. The reason(s) for this behaviour are of considerable interest for further investigations.
4. DISCUSSION Both the morphology and the extreme pit depth data presented herein show that the severity of pitting corrosion of 6060 aluminium alloy after two years’ exposure in immersed natural seawater increases with immersion depth. It is possible that this may be the result of minor differences in factors, such as dissolved oxygen, pH, temperature, etc. (Szklarska- Smialowska 1986).
As is well known, when exposed to air, the surface of aluminium or aluminium alloys immediately develops a thin and compact protective oxide layer (Frankel 1998). The surface layer of seawater contains a higher degree of dissolved oxygen and helps to maintain and re-heal the oxide layer if there are any disturbances (Southwell et al. 1964). Further, there may be slight differences in marine growth and differences in the amount attached to the strips , perhaps providing different degrees of protection over a certain exposure period (Vargel 2004). These reasons may explain why the pitting behaviour of aluminium differs between exposure locations.
The build-up of corrosion products plays an important role in the progression of corrosion with time. As previously reported (Melchers 2010), as pitting increases, the thickness of build-up corrosion products also increases. Since the topography of corroded surface becomes non-uniform and non-homogenous, the corrosion products also become non-uniform and non-homogenous (see Fig. 8 and Fig. 9). As a result, in some extreme cases, localised anoxic conditions gradually form under the corrosion products, which drives the corrosion mechanism to transform gradually from oxygen reduction as the primary cathodic reaction to hydrogen ion reduction dominated corrosion (Melchers 2014a). The reactions under the anoxic conditions are: cathode:
H O→OH +H , 2H +2e →H ↑ (2) anode:
Al → Al +3e (3)
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total reaction:
2Al + 3H O→Al O +3H ↑ (4)
Figure 8 Cross-section SEM view of the maximum pit of coupons immersed at 640mm depth. Scale bar=500μm.
Once an anoxic condition forms, pitting corrosion is considered as an autocatalytic process with the cathodic dissociation of water and effusion of gaseous hydrogen and the anodic oxidation of aluminium. Pitting corrosion rate under this condition, corresponding to the beginning of phase 3 in Fig.1, accelerates. This change in corrosion mechanism may be responsible for the extreme value behaviour shown in Fig. 7 (b) but it clearly remains a matter for further investigation. It may also be a reason as to what caused the very high values of the two maximum pits for coupons immersed at 640mm depth (such as point ‘a’ in Fig. 7 (a)) and why these are so much deeper than the other extreme pits depth and almost four times deeper than the pit depths for coupons immersed at 160mm depth (point ‘b’ in Fig. 7 (a)). Again, these are matters for further investigation, noting that they appear not previously to have been observed in experimental programs.
Figure 9 Cross-section SEM view of the maximum pit of coupons immersed at 160mm depth. Scale bar=200μm.
The ‘step-wise’ topography mentioned in relation to the pits shown in Fig. 6 is illustrated further in the cross- sectional views in Fig. 8 and Fig. 9. A similar phenomenon has been observed previously for pitting on mild steel in seawater (Jeffrey and Melchers 2007). It has been considered as a process by which pits develop in depth to their maximum potential and then grow sideways to amalgamate, after which new pitting can commence. This de-passivation and re-passivation scenario is assumed to contribute to the ‘step-wise’ morphology shown in Fig. 8 and Fig. 9. During pitting, breakdown and repair of the passive film occur simultaneously (Szklarska-Smialowska, 1986). Pits initiate when the rate of de-passivation overtakes re- passivation, thereafter they grow in depth until localized environment changes (e.g. acidic environment in the pit is interfered by concentration of other ions and cracks and/or dissolution of corrosion products (Pride, 1994))
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and then re-passivated. Because of the non-uniform topography at the bottom of pit, new micro-pits have high probability to form at the pre-corroded surface when the acidification is reached (Amin, 2009). Hence, a larger pit may form by coalescence of the micro-pits. The subjectively drawn yellow dashed lines are considered to be the possible bottom of pit at which small pits develop. Moreover, corrosion products within the pits shown in Fig. 8 (right) are significantly thicker and more topographically non-uniform compared with other pits. This might also account for the formation of extremely deep pits (point a in Fig. 7 (a)).
Corrosion products analysis of cross-sections (such as selected from Fig. 8 where is marked with red circle) is displayed in Fig.10. Oxygen is the main element except for aluminium. Chlorine was not found within the vicinity of corrosion products. This result would suggest that, for the extreme pit shown in Fig. 8, the dissolution of aluminium substrate is not a result of attack by aggressive chloride ions, as usually claimed in the literature (Guillaumin and Mankowski 1999). Instead, reactions (2) to (4) are considered to be the main cause of corrosion in this stage and the maximum pits on coupons immersed 640mm under low tide water level is a result of pit growth under anoxic conditions.
Figure 10 Cross-section SEM morphology (scale bar=30μm) of pit and EDS spectrum across the interface of matrix and corrosion products.
Because of the combined influences of build-up corrosion products, immersion depth and other factors such as marine growth and dissolved oxygen content, the variability of extreme pit depth data as measured by 6060 aluminium immersed 2 years in natural seawater shows a significant randomness (see Fig. 11). Comparatively, the extreme pit depths of coupons immersed deeper in the water tend to have higher uncertainty than that near the surface water. this difference in data variability between different immersion depth as well as different directions of location indicates that the corrosion of 6060 aluminium under the seawater is a significantly complicate progress and corrosion mechanisms may include pitting corrosion, crevice corrosion and galvanic corrosion at the same time. This remains a topic for further research. Moreover, the average extreme pit depth data (black bold line) shows a gradually decreased trend as immersion depth increases. Though the high value of average for strip A is a result of the two maximum pit depths, in general, pitting corrosion of 6060 aluminium alloys increases with the depth of seawater.
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Figure 11 Variability and average of extreme pit depth as measured by 6060 aluminium coupons.
5. CONCLUSIONS The morphology of extreme pits for 6060 aluminium alloy coupons exposed two years in natural seawater supports the notion of a ‘step-wise’ change in corrosion mechanism where small pits initiate and grow from the bottom of pre-existing pits.
The data of extreme pit depth, for coupons immersed at both exposure depths, is found to be inconsistent with Gumbel probability distribution. Instead, it shows a non-linear trend. This result is assumed to be a result of change in corrosion mechanism due to build-up corrosion products, which is of considerable interest for further investigation.
Pitting severity and the maximum pit depth increased with the depth of immersion. The maximum pit depth from the data set immersed at 640mm depth (2.023mm) is found to be nearly five times of that at 160mm depth (0.472mm). This may be a result of accelerated corrosion behaviour under anoxic condition. Generally, as immersion depth increases, the variability of pit depth data increases and the average extreme pit depth of 6060 aluminium alloys increases as well.
6. ACKNOWLEDGMENTS The financial support provided by the University of Newcastle and the Australian Research Council is gratefully acknowledged.
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8. AUTHOR DETAILS Mengxia Liang is a first year PhD student at The University of Newcastle, Australia. She holds a Master’s degree in Material Processing Engineering and is studying structural engineering. Her research topic is the long-term pitting corrosion behaviour of aluminium and its alloys.
Igor A Chaves is a research academic at the University of Newcastle, Australia. He is the recipient of the 2012 Marshall Fordham Award (Australasian Corrosion Association) and the 2011 Young Corrosion Professional Award. He is a civil engineer and holds a Master’s degree in structural engineering and a PhD for his studies in corrosion analysis and prediction. His research interests also include structural reliability and risk assessment of infrastructure
Robert E Melchers is Professor of Civil Engineering and ARC DORA Research Fellow at The University of Newcastle, Australia. He holds a BE and M Eng Sc from Monash University and a PhD from the University of Cambridge, UK. He is a Fellow of the Australian Academy of Technological Sciences and Engineering and an Honorary Fellow of The Institution of Engineers Australia.
His most recent awards are the 2009 ACA Corrosion Medal, the 2012 Jin S Chung Award (International Society of Offshore and Polar Engineers) and the 2013 John Connell Gold Medal (The Institution of Engineers, Australia). He was the 2014 Eminent Speaker for the College of Structural Engineers, The Institution of Engineers, Australia.
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