The work focuses on assessing the optimal machining conditions which could simultaneously satisfy multiple process performance indices during machining of AISI D2 steel. The main characteristic indices that have been considered here for evaluating plasma arc machining are surface roughness and material removal rate; the corresponding machining parameters are cutting speed, gas pressure and torch height. The study proposes an integrated optimization module combining fuzzy inference system, nonlinear regression and JAYA algorithm towards optimizing correlated multi-response features during machining of AISI D2 steel. Optimum value of machining parameters found as cutting speed of 4000 m/min, gas pressure of 95 psi and torch height of 0.5 mm using aforementioned methodology. Application potential of the aforesaid integrated optimization route has been compared to that of teaching-learning based optimization (TLBO) algorithm and genetic algorithm. It has been concluded that JAYA algorithm possesses less convergence time and hence execution is faster as compared to TLBO and genetic algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.