Comparative Performance Analysis on WEDM Responses for Titanium Matrix Composite Using Novel Multi-objective Optimization Algorithms

A comparative performance analysis has been investigated on wire-cut electrical discharge machining (WEDM) responses while machining a hybrid titanium matrix composite (TMC) varying the key input parameters like power (P), peak current (IP) and time-off (Toff). Two novel multi-objective optimization algorithms are developed namely desirable multi-objective genetic algorithm (DMOGA) and desirable particle swarm optimization (DPSO). The principal advantage of DMOGA to other algorithm is accuracy and robustness. The novelty fits in the iterative progression of growth of efficient grandee set, uttered as population congregating to a fitness function. DPSO is an enthralling computational method depending on the social behavior of birds and fish where 'swarm' of potential solutions termed as particles explores the problem in space for obtaining the multi-objective optimization (MOO) solution where the desirable objective function is fetched in python using PSO. Experimental investigation is accepted on material removal rate (MRR), surface roughness (SR), kerf width (KW) and over cut (OC). Combined desirability in case of DMOGA is 0.716 which improved to 0.813 when DPSO is proposed. MOO is improved with DPSO of 13.547% when contrasted with DMOGA, with MRR of 3.81 mm3/min, SR of 0.79 mu m, KW of 0.349 mm, OC of 0.099 mm and combined desirability of 0.813. Improved optimality set is obtained when DPSO is used. %improvement of MRR is 5.54%, SR is 75.95%, KW is 0.29% and OC is 4.21%.