Novel statistical investigation on performance measures of WEDM: Optimization, microstructure and mechanical properties

Abstract

A comparative performance analysis has been investigated on wire 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 multi-objective giant pacific octopus optimizer (DMOGPOO) for tackling various issues in many industries like automobile valve pins in crank and cam shafts, aerospace propeller and biomedical implants. 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. Many techniques frequently encounter substandard solutions when evaluating MOO problems, as opposed to solving properly approximated functions of Pareto optimal solutions in targets. DMOGPOO is an enthralling statistical method which mimics the octopus’s predatory behavior, performs better than other multi-objective optimization (MOO) algorithms, where the desirable objective functions is fetched in python using MOGPOO. In a multi-objective foraging environment, the archive was utilized to imitate octopus predatory behavior and establish social hierarchies. DMOGPOO approach is designed with multi-objective formulations to preserve and guarantee enhanced coverage of optimum solutions across all goals. 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 DMOGPOO is proposed. MOO is improved with DMOGPOO of 13.547% when contrasted with DMOGA, with MRR of 3.81 mm3/min, SR of 0.79 µm, KW of 0.349 mm, OC of 0.099 mm, and combined desirability of 0.813. Improved optimality set is obtained when DMOGPOO is used. %improvement of MRR is 5.54%, SR is 75.95%, KW is 0.29%, and OC is 4.21%.