Simultaneous optimization of material removal rate and surface roughness for WEDM of WC-Co composite using grey relational analysis along with Taguchi method

Abstract

In this paper, wire electrical discharge machining of WC-Co composite is studied. Influence of taper angle, peak current, pulse-on time, pulse-off time, wire tension and dielectric flow rate are investigated for material removal rate (MRR) and surface roughness (SR) during intricate machining of a carbide block. In order to optimize MRR and SR simultaneously, grey relational analysis (GRA) is employed along with Taguchi method. Through GRA, grey relational grade is used as a performance index to determine the optimal setting of process parameters for multiple machining characteristics. Analysis of variance (ANOVA) shows that the taper angle and pulse-on time are the most significant parameters affecting the multiple machining characteristics. Confirmatory results, proves the potential of GRA to optimize process parameters successfully for multi-machining characteristics.

Multi-objective optimization in WEDM of D3 tool steel using integrated approach of Taguchi method & Grey relational analysis

“…Finally conclude that by increasing the pulse on time the MRR also increases and vice versa. Jangra et al (2011) investigated the Influence of taper angle, peak current, pulse-on time, pulse-off time, wire tension and dielectric flow rate for MRR and surface roughness during machining of WC-Co composite. In order to optimize MRR and surface roughness GRA is used along with Taguchi method.…”

Simultaneous optimization of WEDM responses using grey relational analysis coupled with genetic algorithm while machining EN 353

“…Huang and Liao [11] carried out MO optimization with conflicting objectives to yield a set of solutions known as trade-off, nondominated, noninferior, or pareto-optimal solutions. Shajan and Shanmugam [12], Chiang and Chang [13], Ramakrishnan and Karunamoorthy [14], Mahapatra and Amar [15], Prasad and Gopala [16], Saurav and Siba [17], Muthu et al [18], Kamal et al [19], Susanta and Shankar [20], Kamal et al [21], Balasubramanian and Ganapathy [22], Somashekar et al [23], Kapil and Sanjay [24], Nixon and Ravindra [25], Kamal et al [26], Neeraj et al [27], Bagherian et al [28], and Rao and Krishna [29] also reported on MO optimization. An easy way of solving MO optimization problem is converting MO optimization problem into SO optimization by multiplying weights to individual objectives or using grey entropy method.…”

“…Hajela and Lin [32] proposed the WBGA for multi-objective optimization. Each solution x i in the population uses a particular weight Konda et al [5] Taguchi (L 9 )→S/N ratio→regression→nondominated point Single MS, SR Puri and Bhattacharyya [6] Taguchi (L 27 )→ANOVA→F test Single MRR, SR, DD Huang and Liao [11] Taguchi (L 18 )→grey→ANOVA→F test Multi MRR, SR, GW Shajan and Shanmugam [12], Taguchi (L 18 )→regression→NSGA Multi CV, SR Chiang and Chang, [13] Taguchi (L 18 )→grey Multi MRR, SR Manna and Bhattacharyya [7] Taguchi (L 18 )→ANOVA→Gauss elimination model Single MRR, SR, SG, GC Ramakrishnan and karunamoorthy [14] Taguchi (L 16 )→S/N ratio→Weighting (MRSN)→ANOVA Multi MRR, SR, WWR Mahapatra and Amar [15] Taguchi (L 27 )→S/N ratio→regression→GA→weighting factor Multi MRR, SR Prasad and Gopala [16] Factorial CCD (32)→ANOVA→regression→NSGA Multi MRR, SR Saurav and Siba [17] Taguchi (L 27 )→grey→ANOVA Multi MRR, SR, kerf Muthu et al [18] Taguchi (L 9 )→grey→ANOVA Multi MRR, SR, kerf Kamal et al [19] Taguchi (L 18 )→grey→ANOVA Multi MS, SR, DD Susanta and Shankar [20], Taguchi (L 18 )→S/N ratio→Grey→MRSN→WSN→ANOVA Multi MRR, SR, kerf Kamal et al [21], Taguchi (L 18 )→grey→ANOVA Multi MRR, SR Balasubramanian and Ganapathy [22] Taguchi (L 8 )→grey→ANOVA Multi MRR, SR Somashekhar et al [23] Taguchi (L 9 )→grey→ANOVA Multi MRR, SR, overcut Kapil and Sanjay [24] Taguchi (L 27 )→S/N ratio→regression→NSGA-II Multi MRR, SR Nixon and Ravindra [25] Taguchi (L 16 )→ANOVA→regression→GA Multi MRR, SR, DE Kamal et al [26] Taguchi (L 18 )→grey+entropy→ANOVA Multi MRR, SR, AE, ROC Neeraj et al [27] RSM (32)→ANOVA→regression→desirability Multi CS, DD Zhang et al [8] RSM (32)→ANOVA→regression→BPNN-GA Single MRR, SR Bagherian et al [28] Taguchi (L 27 )→ANFIS→GRA Multi CV, SR Rao and Krishna [29] Taguchi (L 27 )→ANOVA→regression→NSGA-II Multi MRR, WWR MS machining speed, DD dimensional deviation, GW gap width, CV cutting velocity, SG spark gap, GC gap current, AE angular error, ROC radial overcut, CS cutting speed, BPNN-GA backpropagation neural network combining with genetic algorithm, ANFIS adaptive neuro-fuzzy inference system vector w i ={w 1 , w 2 ,….w k } in the calculation of summing objective function. The weight vector w i is embedded within the chromosome of each solution.…”

Multi criteria decision making of machining parameters for Die Sinking EDM Process

“…Jangra (2012) presented a study on un-machined surface area named as surface projection, in die cutting after rough cut in WEDM process. Jangra et al (2011) studied wire electrical discharge machining of WCCo composite. Influence of taper angle, peak current, pulse-on time, pulse-off time, wire tension and dielectric flow rate are investigated for material removal rate (MRR) and surface roughness (SR) during intricate machining of a carbide block.…”