OPTIMIZATION OF MATERIAL REMOVAL RATE AND SURFACE ROUGHNESSIN WED-MACHINING OF TiNi SMA USING GREY RELATION ANALYSIS

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1 OPTIMIZATION OF MATERIAL REMOVAL RATE AND SURFACE ROUGHNESSIN WED-MACHINING OF TiNi SMA USING GREY RELATION ANALYSIS Manjaiah M 1*, Narendranath S 2, Basavarajappa S 3 1* Dept. of Mechanical Engineering, NITK, Surathkal, , manjaiahgalpuji@gmail.com 2 Dept. of Mechanical Engineering, NITK, Surathkal, , snnath88@yahoo.co.in 3 Dept. of Mechanical Engineering, UBDT college of Engg., Davangere, , basavarajappas@yahoo.com Abstract In the present paper, wire electro discharge machining (WEDM) of TiNi shape memory alloy (SMA) is studied. Influence of pulse on time, pulse off time, servo voltage, dielectric fluid pressure and wire speed are investigated for material removal rate (MRR) and surface roughness (Ra) during machining of a stepped TiNi beam. To optimize the MRR and Ra simultaneously, grey relational analysis (GRA) is employed with Taguchi L 27 orthogonal array. Through GRA, grey relation grade is used as performance index to find the optimal process parameters for the machining characteristics (MRR and Ra). Analysis of variance (ANOVA) shows that the pulse on time is the most significant parameter affecting the MRR and Ra. Confirmation results proves the potential of GRA to optimize the multi machining characteristics of WEDM process parameters. Keywords:WEDM,TiNi SMA, MRR, Ra, Multi optimization, GRA, L 27 Orthogonal array 1. Introduction TiNi shape memory alloy (SMA) is an important class of material, used in various fields such as medical, micro engineering and commercial sectors due to excellent wear, outstanding corrosion resistance and biocompatibility properties. Furthermore, SMA has often been classified into difficult-to-machine material due to low thermal conductivity, which in turn hinders quick dissipation of heat caused by machining and thus leading to higher tool wear and severe strain hardening (Weinertand Petzoldt 2004, Lin et al., 2000). Hence, due to difficulty in conventional machining of TiNi SMA, the non-traditional machining is usually performed using special machining techniques such as electric discharge machining (EDM) (Alidoosti et al., 2013) and wire electric discharge machining (WEDM) (Hsieh et al., 2009). It is a thermo electric process; material removal takes place by the discrete sparks discharge between the electrode and workpiece. The electric sparks melt and vaporizes some amount of material from the workpiece, which is flushed away from the workpiece surface by the dielectric pressure. The selection of optimum machining parameter combination to obtain higher material removal rate (MRR) and better surface roughness (Ra) is a difficult task in WEDM because of numerous input process parameters and stochastic process. Hence in the present study Taguchi method and grey relation approach is used optimize simultaneously the responses of MRR and Ra of WEDM of TiNi SMA. 2. Experimental setup and design In the present work, five parameters, namely, pulse on time, pulse of time, servo voltage, flushing pressure and wire speed were identified and the range of the each parameter was determined from the preliminary experiments. Each process parameter was investigated at three levels to study the non-linearity effect of the parameters. The brass wire (Ø0.25mm) was used as electrode, which consists of 65% zinc and 35% copper. Fixed parameters such as peak current of 14A, short pulse time of 0.2µs and wire tension is of 1daN. The experiments were performed on Robofill -290 WEDM (Make: Charmills Co.) as per L 27 orthogonal array (Ross, 1996), the identified controllable parameters in WEDM of TiNi SMA experiments and their associated levels and the experimental layout plan is shown in Table 1. Based on the experimental layout shown in Table 1, the experiments were performed. The machining characteristics material removal rate (MRR) and surface roughness (Ra) were measured. MRR was calculated by the volume of material removed with respect to time and Ra was measured in a Mitutoyo surface roughness tester. The mean values and S/N ratios are depicted in Table

2 OPTIMIZATION OF MATERIAL REMOVAL RATE AND SURFACE ROUGHNESSIN WED-MACHINING OF TiNi SMA USING GREY RELATION ANALYSIS Sl. No. Pulse on time (µs)a Pulse off time (µs)b Table 1: Experimental plan with the responses and corresponding S/N ratios Servo voltage (V) C Flushing pressure (bar) D Wire speed (m/min) E MRR (mm 3 /min) S/N ratio (MRR) Surface Roughness (µm) S/N ratio (Ra) Results and discussion 3.1 Analysis of variance for MRR Table: 2 ANOVA for MRR based on S/N ratio Parameter DOF SS MS % contribution A B C D E Error Total % confidence level, DOF- Degrees of freedom, SS- Sum of squares, MS- Mean square The analysis of variance (ANOVA) depicts the relative significance of each individual process parameter and their corresponding percentage of contribution towards the response. ANOVA is performed using S/N ratios and to obtain the percentage contribution of each individual process parameter. It is found that from Table 2 Pulse on time (A) has a most significant influence and it can be seen that the contribution is 81.90% on the MRR and pulse off time, servo reference voltage and wire speed has less significant on MRR. The wire speed has no effect on the MRR. Higher the pulse on time higher will be the MRR due to increased discharge energy, longer time to melt and intensity of spark. 3.2 Analysis of variance for Surface roughness Table: 3 ANOVA for MRR based on S/N ratio Parameter DOF SS MS % Contribution A B C D E

3 Error Total % confidence level, DOF- Degrees of freedom, SS- Sum of squares, MS- Mean square Analysis of variance for surface roughness justifies the goodness of fit. The results of ANOVA for Ra are presented in the Table 3. ANOVA calculated for 95% of confidence level. The aim is study the which are the significant machining parameters affecting the surface roughness or not. From the Table 3 it analyzed that pulse on time have greater influence on surface roughness and also it shows that pulse off time, servo voltage and flushing pressure affecting the surface roughness. 3.3 Multi response optimization using grey relation Grey relation data processing is performed by using S/N ratio of experimental data. In this study, a linear normalization of the S/N ratios for MRR and Ra were performed. A linear data pre-processing method for the S/N ratio can be expressed by following relation as found in (1) below: ( )= ( ) ( ) ( ) ( ) (1) Where y i * (k) is the sequence after the data processing; ( ) is original sequence of S/N ratio, i = 1,2,3,...,m and k = 1,2,3,...,n with m = 27 and n = 2; max ( ) is the largest value of ( ); min ( ) is the smallest value of ( ). The outcomes are represented as ( ) and ( )for reference sequence and comparability sequence, respectively. Basically, the larger normalized S/N ratio corresponds to the better performance and the best-normalized S/N ratio is equal to unity. Followed by data processing, it is necessary to establish the relationship on ideal versus actual normalized values. It is calculated by calculating the grey relational coefficient, which is expressed in the equation (2) [6]. ( ). ( ) =. ( ). 0 ( ). ( ) (2) Where ( ) is the deviation sequence of reference sequence ( ) and comparability sequence ( ) i.e. ( ) = ( ) ( ) is the absolute value of the difference between ( )and ( ), = max. max. ( ), is the distinguishing coefficients 0,1. is set as 0.5 in this study. The purpose of defining this coefficient is to show the relational degree between the reference sequences ( ) and comparability of 27 sequences ( ), where i = 1, 2, 3... m and k = 1, 2, 3,..., n with m = 27 and n = 2 in this study. Using data processing values the deviation sequence can be calculated as follows (Jangra et al., 2010), (1) = = (2) = = Then, = ( , ). = (1) = (2)= = (1) = (2)= According to deviation sequence and equation (2), the grey relational coefficient ( ). ( ) are calculated as follows:... (1). (1) =... = (2). (2) =... = The grey relational grade is a weighting-sum of the grey relational coefficients. The overall evaluation of multiple performance characteristics is based on the grey relational grade and it is defined as follows, (. )= ( ). ( ) (3) Where represents the weighting value of the k th performance characteristics, and = 1. Using the same weighing values of MRR and Ra as were assigned in utility analysis (i.e. w 1 = w 2 = 0.5), the grey relational grade are calculated. Table 4 lists down the response table for grey relation coefficients and grey relation grade corresponding to machining parameters. = min. min. ( ), 351-3

4 OPTIMIZATION OF MATERIAL REMOVAL RATE AND SURFACE ROUGHNESSIN WED-MACHINING OF TiNi SMA USING GREY RELATION ANALYSIS Table: 4 Response table for grey relation coefficients and grade Grey Relational Co-efficient Grey Sl. No. relation MRR Ra grade Fig. 1 Graph of gray relation grade According to performed experiment design, it is clearly observed from Table 4 and Fig. 1 that the WED-machining parameters setting of experiment no. 26 has the highest grey relation grade. Thus, the 26 th trial of experiment gives the best multiperformance characteristics among the 27 experiments. 3.4 Optimal level of process parameters Calculation of grey relation reasoning grade is carried out to obtain optimum parameters for obtaining higher MRR and lower surface roughness. Optimization of the multiple performance characteristics can be converted into optimization of single grey relational grade. It is clearly observed from Table 3 for grey relational grade, the process parameters setting of trial 26 has the highest grey relational grade. A 3 B 3 C 2 D 1 E 2 combination shows higher grey relational grade value hence as per GRA these combination of parameters gives the optimal process parameter setting. This may be considered as an optimal combination of WEDM process parameters so as to produce desired values of the performance characteristics. Therefore, A 3 (1µs), B 3 (25µs), C 2 (40V), D 1 (1.8 kg/cm 2 ) and E 2 (8N) is the optimal parameter combination for multi-quality characteristics. Fig. 2 Response graph for each level of the WEDmachining parameters

5 During the machining in WEDM process, the value of grey relational grade for each operating parameter concerning the MRR and surface roughness is the greater the better. Fig. 2 shows the response graph of the total mean of the grey relational grade. The highest steep slope of response graph indicates the more influencing operating parameter in the multi response performance characteristic. The front four operating factors namely pulse on time (A), pulse off time (B), servo voltage (c) and Flushing pressure (D) have highest significance on the output responses. From the analysis of variance it is analysed that pulse on time, pulse off time and servo voltage having noticeable resources of influential parameters on the improvable quality characteristics. The other parameters are considered as unnoticeable effect on the output responses. Based on the above discussion, the optimum cutting parameter levels of A 3 B 3 C 2 D 1 E 2 for both the performance characteristics of MRR and surface roughness simultaneously. 4. Conclusions In the present work, wire electro discharge machining characteristics of TiNi SMA has been studied. Taguchi based grey relation analysis is used to optimize the MRR and surface roughness (Ra), simultaneously. Based on the experimental analysis the following conclusions are made. 1. From the analysis of variance (ANOVA) the process parameters pulse on time, pulse off time, and servo voltage are the major influencing parameters on the MRR and surface roughness. 2. Pulse on time is the major influencing parameter for both the responses MRR and surface roughness. Increase in pulse on time and servo voltage increased MRR and surface roughness was achieved. 3. Grey relational analysis was used to determine the optimal combination of process parameters for multiple machining characteristics (MRR and Ra). Equal weights were assigned to both the machining characteristics in calculating the grey relational grade. The A 3 B 3 C 2 D 1 E 2 combination ofparameters level provides an optimal machining characteristic. References Weinert K, PetzoldtV(2004), Machining of NiTi based shape memory alloys. Materials Science Engineering A, 378: Lin HC, Lin KM, Chen YC. A (2000), study on the machining characteristics of Ti 50 Ni 50 shape memory alloys.journal of Materials Processing Technology,Vol 105, pp Alidoosti, Ghafari-Nazari A, Moztarzadeh F, Jalali N, Moztarzadeh S, Mozafari M (2013) Electrical discharge machining characteristics of nickel-titanium shape memory alloy based on full factorial design. Journal of Intelligent Material Systems and Structures, Vol, pp1 11. Hsieh SF, Chen SL, Lin HC, Lin MH, Chiou SY (2009), The machining characteristics and shape recovery ability of Ti Ni X (X=Zr, Cr) ternary shape memory alloys using the wire electro-discharge machining. International Journal of Machine Tools and Manufacture,Vol49, pp Ross PJ (1996), Taguchi Techniques for Quality Engineering, McGraw-Hill, New York. Jangra K, Jain A, Grover S (2010), Optimization of multiple-machining characteristics in wire electrical discharge machining of punching die using Grey relational analysis.industrial Research, Vol 69,pp