Optimization of Wire Edm Process Parameters for Medical Grade Nickel Titanium Shape Memory Alloy

Strojnícky časopis – Journal of MECHANICAL ENGINEERING, VOL 70 (2020), NO 1, 69 - 80

OPTIMIZATION OF WIRE EDM PROCESS PARAMETERS FOR MEDICAL GRADE NICKEL TITANIUM SHAPE MEMORY ALLOY

VINAYAK N Kulkarni1, V N Gaitonde2, K S Nalavade3, MRITYUNJAY Doddamani4, GAJANAN M Naik5

1 School of Mechanical Engineering, KLE Technological University, Vidyanagar, Hubballi,580031, Karnataka, India. e-mail: [email protected] 2 School of Mechanical Engineering, KLE Technological University, Vidyanagar, Hubballi,580031, Karnataka, India. e-mail: [email protected] 3 School of Mechanical Engineering, KLE Technological University, Vidyanagar, Hubballi, 580031, Karnataka, India. e-mail: [email protected] 4 Advanced Manufacturing Laboratory, Department of Mechanical Engineering, National Institute of Technology, Surathkal, Karnataka, India.e-mail: [email protected] 5 Department of Mechanical Engineering, Mangalore Institute of Technology and Engineering, Moodbidri, 574225, Karnataka, India. e-mail: [email protected]

Abstract: Nickel Titanium (NiTi) alloys are the class of smart materials classified under shape memory alloys. The traditional machining of these alloys is hard because of various inherent mechanical characteristics of these alloys. Therefore, non-traditional machining process such as wire electro discharge machining (WEDM) has been employed for machining of such alloys. The present study is focused on multi-performance characteristic simultaneous optimization of WEDM process parameters, in which three system control factors, namely, pulse on time (TON), pulse off time (TOFF) and wire feed (WF) are considered for simultaneously maximizing material removal rate (MRR), while minimizing surface roughness (SR) and tool wear rate (TWR). The simultaneous optimization is performed using Taguchi’s Quality Loss Function. Analysis of means and analysis of variance have been carried out to identify the significance level of each system control factor. The different levels of settings and the optimized setting have been analysed using scanning electron microscope images for surface morphological studies. The multi-response optimization investigations revealed that TON is the major contributing factor and optimal performance values were obtained at TON of 125μs, TOFF of 25μs and at WF of 4 m/min.

KEYWORDS: Nickel Titanium; shape memory alloys; Simultaneous optimization, Quality loss function

1 Introduction Nickel-Titanium(NiTi) or Nitinol alloys are the unique group of shape memory alloys (SMA), which are in continuous demand from last decade due to advanced material characteristics such as higher corrosion resistance, shape memory effect, super elasticity, biocompatibility etc [1,2]. As these alloys have different types of properties, they are successfully used in variety of applications in the area of defence, aerospace, automobile and especially in biomedical engineering [3]. These NiTi SMAs possess high ductility, high strength, poor thermal conductivity followed by higher tool wear and hence non-traditional machining processes are much preferred over traditional machining for such alloys [4]. Wire electro discharge machining (WEDM) is one such non-traditional machining and electro- thermal process used to machine hard and conductive materials [5]. WEDM is a preferred choice of researchers and industry people for machining hard and conductive materials with complicated and intricate shapes [6,7]. NiTi SMA is also hard and conductive material that requires a non-contact type machining for retaining its shape memory effect even after machining process [8]. As the NiTi SMA, WEDM process, and the related testing facilities

DOI: 10.2478/scjme-2020-0007, Print ISSN 0039-2472, On-line ISSN 2450-5471 ©2020 SjF STU Bratislava are costly in terms of economy, it is very much essential to have an extensive study on optimization of WEDM process input parameters for machining of NiTi SMA [9,10]. Optimization of process parameters have been carried out using different techniques like Taguchi, central composite design etc. [11,12]. Researchers have worked in the area of machining of NiTi SMA with few non-traditional machining processes like WEDM [13]. Few authors studied the individual effects of input process parameters (control factors) on output responses (quality characteristics) of WEDM of binary and ternary SMA. Pulse on time(TON), pulse off time (TOFF) and peak current are found to be the most varied input parameters among many researchers, whereas, material removal rate (MRR) and surface roughness (SR) are the major output responses studied [14,15]. Similarly, authors have reported few studies on optimization of WEDM input process parameters for NiTi SMA. Many researchers have worked on single response optimization technique, whereas very few studies have been reported on multi-response characteristic optimization technique using Taguchi’s utility and modified utility concepts [16,17]. Most of the findings from those studies showed that higher TON with lower peak current decreases SR, whereas peak current do not have much influence on MRR [18,19]. Some experimental studies show that discharge current, pulse on time and conductivity of work materials are the more influencing factors affecting MRR [20,21]. From the literature available, it is clear that most of the researchers have concentrated on modelling of WEDM process parameters for machining of NiTi binary and ternary alloys, whereas there are very few reports on multi-performance characteristics simultaneous optimization of WEDM process parameters for machining of NiTi SMA and there are hardly no studies on input parameters like wire feed (WF) and output responses like tool wear rate (TWR). Therefore, chief objective of the present study is to investigate the optimum input process parameters of WEDM such as TON, TOFF and WF for simultaneously maximizing MRR and minimizing TWR and SR using zinc coated brass wire as wire electrode material, while machining NiTi medical grade SMA through multi response characteristic simultaneous optimization technique like quality loss function.

2 Material and Methodology

2.1 Work Material Nickel-Titanium medical grade shape memory alloy with ASTMF2063 standard specification has been used for the present experimental study. The work material has been imported from certified material supplier called Baoji Hanz Materials Technology Co. Ltd, China. The test report of the material confirmed the presence of Ni with 55.74% and Ti as remainder with other supporting chemical compositions as per ASTM standards for a medical grade NiTi shape memory alloys. The medical grade NiTi SMA has an austenite finish temperature of 35+10oC with tensile strength and yield strength of 825 MPa and 202 MPa respectively. Medical grade nickel titanium alloy find its applications in various biomedical field such as mandible fracture plates, bone plates, dentistry, stents etc.

2.2 Experimental Setup The experimental studies were conducted on Electronica make Ecocut CNC WEDM machine with zinc coated brass wire of 0.25 mm as wire electrode material. The experiments were carried out at the central facility called “Makers Space” at KLE Technological University, Hubballi-INDIA. The medical grade NiTi SMA work material procured with dimensions 181mm×600mm×2mm has been initially cut into 181mm×100mm×2mm dimensions for performing the experiments. The size of NiTi plate has been reduced as to accommodate the work material into the WEDM machining zone. A 10mm diameter circle has been chosen as cutting profile to suite the requirement of a biomedical applications such

Fig.1 Experimental setup

2.3 Experimental Design and Methodology

Taguchi’s L9 orthogonal array (OA) is used to plan the experiments. Taguchi’s Quality Loss Function (TQLF) is deployed for multi-response simultaneous optimisation of WEDM control factors. TQLF estimates the overall loss of quality occurred because of the deviation of the quality characteristics from its desired value [20]. Analysis of means (ANOM), Analysis of variance (ANOVA) and simultaneous signal to noise (S/N) ratio plots are utilised to discover the effects of factors. Confirmatory investigations were carried out for the validation of the proposed model to simultaneously maximize the MRR and minimize SR and TWR. Further, scanning electron microscopy (SEM) micrographs are studied to analyze the surface-morphology of machined surface.

2.4 Control Factors and their Selection WEDM is extensively used machining method to produce intricate parts and complex profile with high accuracy and ease of machining. The WEDM machine provides freedom for the operator to choose few important parameters from vast number of control factors that can be varied over wide span. TON duration, TOFF duration, servo-voltage, peak current, dielectric flow rate, wire feed, wire tension, table feed rate are some of the control factors which are accessible on the machine. These control factors has to be chosen wisely and any inappropriate selection of these parameters leads to undesirable effects such as uneconomical machining, surface irregularities, high thermal stresses, low MRR etc. Hence, in this research work, the effects of three important control factors on three different quality characteristics viz. MRR, SR and TWR are explored. The control factors for present study are chosen based on thorough pilot study, continues brain storming sessions, authors previous work and extensive literature survey [22]. The three control factors considered for present investigations with their varied levels are illustrated in Table 1, whereas Table 2 summarizes the fixed process parameters and their values.

Table 1. Control factors (input process parameters) and their varied levels Code Control Factors Level 1 Level 2 Level 3 A TON (µsec) 105 115 125 B TOFF (µsec) 25 40 55 C WF (m/min) 4 6 8

Table 2. Fixed process parameters S.No Parameters Values 1 Work Material Medical Grade NiTi SMA 2 Thickness 2mm 2 Wire Electrode Zinc Coated Brass Wire of 0.25 mm 3 Peak Current 12A 4 Dielectric Fluid De-ionised water 5 Servo Feed 2150 mm/min 6 Pulse Peak Voltage 11V 7 Servo Voltage 20V

2.5 Taguchi’s Quality Loss Function for Simultaneous optimization. The TQLF estimates the total quality loss occurred due to deviation of the quality characteristic from the desired value [20]. This tangible value of TQLF is further represented in the form of S/N ratio. Higher the value of S/N ratio, lesser the amount of scatter observed in outcomes. The factors with higher S/N ratio are considered to be optimal. According to the type of quality characteristics i.e. lower the better type (LTBT), nominal the better type (NTBT) or higher the better type (HTBT), the respective equation for the calculation of S/N ratio is deployed. The mathematical equation for LTBT and HTBT type of quality characteristics is as follows.

ƞ = −10푙표𝑔10(푄퐿) (1)

2 1/푍푖푗 , For HTBT type (푄퐿) = { 2 푍푖푗 , For LTBT type th th Were Zij is the measured quality characteristic at the i experiment level of j trial. In simultaneous optimization, instead of calculating S/N ratio for every response variable separately, a single overall S/N ratio for multi-performance characteristic is computed [23]. But most of the times, different response variable have different measuring units and therefore, it is not possible to add loss associated with each response variable. Hence it is imperative to normalize the loss function before evaluating total loss function (TLF). The formula for normalized loss function (NLF) is given in equation (4). The TLF associated with each parametric level is computed by assigning weightage factor for every quality characteristic selected for optimization. These weightage factors majorly depend on the expectations of end user, desired output and priorities among different quality characteristics. The TLF is presented in equation (3). After determining TLF associated with each parametric level, the multi-response S/N ratio is computed by deploying equation (2). Based on the multi response S/N ratio the significance level of each process parameter with its optimal setting are derived by using ANOM, ANOVA and S/N plots.

ƞ푚푟 = −10푙표𝑔10(푇퐿퐹푗) (2)

푛

푇퐿퐹푗 = ∑ 푤푖(푁퐿퐹)푖푗 (3) 푖=1

푁퐿퐹푖푗 = 푄퐿푖푗⁄푄퐿 (4) th Where ƞmr is multi response S/N ratio in dB, TLFj is total loss function for j trial, wi is the weightage factor of ith response variable, n represents total number of response variables, th th th NLFij is the normalised loss function for i setting of j trial, QLij is quality loss at the i experimental level of jth trial and 푄퐿 is average quality loss.

2.6 Measurement of Quality Characteristics The quality characteristics considered for present analysis are MRR, SR and TWR. 2.6.1 Material removal rate (MRR) MRR is basically a function of machining time, width of cut and material height or thickness. High accuracy digital stop watch is used for recording the total machining time, whereas, the width of cut is measured using Faro-Gage portable co-ordinate measuring machine and MRR is computed with the help of equation (5). 3 푀푅푅 = 푣푐 × 푏 × ℎ 푚푚 /푚𝑖푛 (5) Where,

Vc =Cutting Speed= πD/(Tm) in mm/min.

Tm =Machining Time in Sec D = Diameter in mm b = Kerf width (Width of cut) in mm h = Plate thickness in mm 2.6.2 Surface roughness (SR) Measurement of SR has been carried out at Zeiss Industrial Metrology Division, Bengaluru, INDIA. Carl Zeiss SURFCOM 1500D2 SR tester is deployed to measure the average SR value (Ra) in µm. The sampling length of the measuring probe is fixed to 1.6mm for all the samples. 2.6.3 Tool Wear Rate(TWR) TWR is proportional to the rate of wire feed and weight of electrode wire before machining and after machining. Weight of the wire before machining and after machining , equal to 20m length are measured and values were recorded accordingly. TWR is calculated with the help of equation (6) 푊 − 푊 푇푊푅 = 푏 푎 × 푊퐹 𝑔푚푠/푚𝑖푛 (6) 20 Where, TWR= Tool wear rate

Wb = Weight of wire before machining in gms Wa = Weight of wire after machining in gms WF= Wire Feed in mm/min.

3 Results and Discussions The SR values (Ra) measured using SURFCOM 1500D2 tester and computed values of MRR and TWR along with Taguchi’s L9 orthogonal array adopted for the studies, has been summarised in Table 3.

Table 3: L9 orthogonal array with response data EXP TON TOFF WF MRR SR (Ra) TWR NO (µsec) (µsec) (m/min) (mm3/min) (µm) (gms/min) 1 105 25 4 2.798 1.7063 0.0128 2 105 40 6 3.050 1.6806 0.0532 3 105 55 8 1.699 1.8240 0.0235 4 115 25 6 3.690 2.0959 0.0301 5 115 40 8 3.428 2.0837 0.0374 6 115 55 4 3.150 1.8748 0.0218 7 125 25 8 6.281 2.5709 0.0851 8 125 40 4 6.200 3.0993 0.0475 9 125 55 6 5.506 2.6619 0.0664 The calculated values of QLF for raw data, NLF; TLF along with corresponding S/N ratios(dB) is tabulated in Table 4. The TLF is calculated by giving equal importance to all the quality characteristics. 33.33% weightage factor considered while computing the TLF. Table 4: Loss functions with Multi-response S/N ratio Multi Exp Total loss Quality loss function Normalised loss function Response No function S/n Ratio QL1j QL2j QL3j NLF1j NLF2j NLF3j TLFj ƞmr dB 1 0.1277 2.9115 0.000 1.2424 0.5873 0.0729 0.6336 1.982 2 0.1075 2.8244 0.003 1.0456 0.5698 1.2589 0.9571 0.190 3 0.3464 3.327 0.001 3.3695 0.6712 0.2456 1.4273 -1.545 4 0.0734 4.3928 0.001 0.7143 0.8862 0.403 0.6672 1.758 5 0.0851 4.3418 0.001 0.8277 0.8759 0.6222 0.7745 1.110 6 0.1008 3.5149 0.001 0.9802 0.7091 0.2114 0.6329 1.987 7 0.0253 6.6095 0.007 0.2465 1.3334 3.2213 1.5988 -2.038 8 0.026 9.6057 0.002 0.2530 1.9378 1.0036 1.0637 -0.268 9 0.033 7.0857 0.004 0.3208 1.4294 1.9611 1.2359 -0.920 To understand the effects of individual control factors ANOM is carried out on multi- response S/N ratio. The results of ANOM are tabulated and plotted. Table 5 represents ANOM summary. Figure 2 represents main effects plots for multi response S/N ratio. More the value of S/N ratio, lesser the scatter observed, hence the factor levels with maximum value of S/N ratio are the optimum levels. From Table 5 and Fig.2 it is evident that optimum level is observed at TON of 115µs, TOFF of 25µs and WF is at 4 m/min. From the results of ANOVA summarised in Table 6 and as per the ranks of the factors shown in Table 5, it is clear that factor A i.e. TON is the major contributing factor with almost 57% contribution followed by rate of wire feed with 33.5% contribution; whereas, TOFF has no significant contribution towards simultaneous optimisation of MRR, SR and TWR.

Table 5. ANOM summary Level/Factor TON (µsec) TOFF (µsec) WF (m/min) 1 0.2091 0.5673 1.2334 2 1.6180 0.344 0.3427 3 -1.0754 -0.1595 -0.8244 Delta 1.4089 0.4078 0.8907 Rank 1 3 2

Table 6. Summary of ANOVA % Factor DOF SS MSS R atio Contribution A 2 10.8892 5.4446 10.72 56.9333 B 2 10.8892 0.4158 0.81 4.3481 C 2 6.3902 3.1951 6.29 33.4105 Error 2 1.0152 0.5076 -- 5.3081 Total 8 19.1262 2.3908 100

Fig. 2 Main effects plots for multi response S/N ratio

3.1 Validation of Proposed Model Validation of the developed (proposed) model is carried out by performing confirmatory experiments. The outcomes of the confirmatory experiments are tabulated in Table 7. While performing confirmatory experiments the values of MRR, SR and TWR are measured and

Volume 70, No. 1, (2020) ©2020 SjF STU Bratislava 75 recorded. Multi-response S/N ratio for confirmation experiments is calculated using equation (2). Prediction error is computed and compared with 2-standard-deviation confidence limit.

Table 7. Outcome of validation experiments Quality measures Outcome

Optimal Levels (A, B, C) 2-1-1

Predicted S/N ratio dB 2.9176

Observed S/N ratio in dB 2.4541

Confidence limit (2σ) in dB ±0.8228

From the outcomes of the adequacy test (Table 7), it is clear that the prediction error is well within the 2-standard-deviation confidence limit. Therefore, the proposed model for simultaneous optimisation of MRR, SR and TWR is adequate with optimal parametric settings of TON=115µs; TOFF =25µs and WF=4 m/min.

3.2 Analysis of Machined Surface Morphology The machined surface morphology of NiTi shape memory alloy at different parameter settings has been analysed through images obtained from scanning electron microscope (SEM). From Fig.3(a, b, c and d), it is observed that the machined surface includes the micro voids, melted debris, micro globules, and cracks at different parametric settings. Indeed, surface morphological study has an important role in understanding the surface characteristic of machined surfaces. Therefore, SEM analysis has been made for analyzing the surface texture of NiTi SMAs after WEDM process to have a proper study and look at the effect of different parameters on surface of NiTi medical grade alloy.

Fig.3 SEM morphology of NiTi SMAs at different parameter settings The machined surface characterizes the surface defects and melted debris of irregular structure orientation due to continuous melting and evaporation during machining shown in Fig.3(a), yields a moderate rough surface, TWR and higher MRR of 6.281mm3/min, this is due to higher TON of 125µs, which increased energy supply leading to higher sparks and hence increased the depth and cracks of micro voids. Also, the micro-globule is shaped by melted metal droplets during the energy discharge and re-solidifies on the NiTi surface. Fig. 3(b) exhibited lower SR of 1.6806µm as seen in Table 3, which includes the minimum number of micro voids and micro globules due to lower TON of 105µs. Fig.3(c) and 3(d) presents the machined surface morphology of component which exhibited minimum TWR and optimized setting parameters respectively. In addition, the surface characteristics depend on the magnitude of the TON during machining. The surface defects and re-solidified debris are more dominant at higher TON and at lower TOFF. Thus, neutral (optimized) control parameters exhibited moderate responses. This can be validated with SEM images showing moderate MRR, TWR and less surface irregularities as shown in Fig.3(d).

CONCLUSION Optimizing any one quality characteristic is not desirable, when the expected outcome is the combination of many different responses. The optimal or nominal conditions derived for one quality characteristic may not be suitable with the optimal values of the other quality characteristics. Moreover, some control factors yielding highest significance while optimizing one response may have negligible or no significant effect while optimizing other responses. Hence there is a need to have a trade-off which will optimize all the quality characteristics simultaneously. In present research work, TQLF has been employed for simultaneously optimizing three important process parameters like TON, TOFF and Wire Feed. The experimental investigations as shown in summary of ANOVA (Table 5) revealed that TON is Volume 70, No. 1, (2020) ©2020 SjF STU Bratislava 77 the major contributing factor with contribution percentage of 56.9333 followed by wire feed as next important contributing factor with 33.4105 percentage, whereas TOFF has very negligible impact with the contribution of just 4.3481 percentage towards simultaneous optimization of WEDM process parameters for NiTi shape memory alloy. The optimal values are obtained at the parametric settings of TON =115µs; TOFF =25µs and WF=4 m/min. The validation experiments conducted unveiled that the developed model is adequate for simultaneous maximisation of MRR and minimization of TWR and SR. Also, the surface morphology study of the machined surface using SEM micro-graphs validated that optimal parametric setting yielded moderately less surface irregularities as compared to other parametric settings. As a future scope, the present work can be expanded by varying the weightage factors of quality characteristics to observe its effects on multi-response S/N ratio to have customized tailored optimal values.

ACKNOWLEDGEMENT The authors would like to thank KLE Society and KLE Technological University, Hubballi - India, for providing the financial support to carry out the research project under capacity building funds.

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