Optimization of cylindrical grinding process parameters using meta-heuristic algorithms

Abstract

Owing to the complexity of grinding process, it has been very difficult to predict the optimal machining conditions which have been resulted in smooth surface finish, accurate geometric measurements and higher production rate. In this work, empirical models for surface roughness, roundness error and metal removal rate have been developed based on regression analysis. These models have been associated the grinding process parameters (work speed, feed rate and depth of cut) with machining performances (metal removal rate, roundness error and surface roughness). Using these models, the optimization has been carried out based on simulated annealing (SA) and genetic algorithm (GA) which have been the two popular meta-heuristic optimization techniques. Finally, the results of the proposed techniques l have compared and experimentally validated.