Grinding process parameter optimization using non-traditional optimization algorithms

Abstract Selection of machining parameters in any machining process significantly affects the production rate, quality, and cost of a component. This paper presents the multi-objective optimization of process parameters of a grinding process using various non-traditional optimization techniques such as artificial bee colony, harmony search, and simulated annealing algorithms. The objectives considered in the present work are production cost, production rate, and surface finish subjected to the constraints of thermal damage, wheel wear, and machine tool stiffness. The process variables considered for optimization are wheel speed, workpiece speed, depth of dressing, and lead of dressing. The results of the algorithms presented are compared with the previously published results obtained by using other optimization techniques.

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