Optimization of cylindrical grinding process parameters using meta-heuristic algorithms
Owing to the complexity of grinding process, it has been very difficult to predict the optimal machining conditionswhich have been resulted in smooth surface finish, accurate geometric measurements and higher production rate. In thiswork, empirical models for surface roughness, roundness error and metal removal rate have been developed based onregression analysis. These models have been associated the grinding process parameters (work speed, feed rate and depth ofcut) with machining performances (metal removal rate, roundness error and surface roughness). Using these models, theoptimization has been carried out based on simulated annealing (SA) and genetic algorithm (GA) which have been the twopopular meta-heuristic optimization techniques. Finally, the results of the proposed techniques l have compared andexperimentally validated.
Cylindrical grinding, Modeling, Optimization, Regression analysis, Simulated annealing, Genetic algorithm
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