Multiobjective Optimization of Grinding Process Parameters Using Particle Swarm Optimization Algorithm

Grinding is one of the very important machining operations in engineering industries. Optimization of grinding processes still remains as one of the most challenging problems because of its high complexity and non-linearity. This makes the application of traditional optimization algorithms quite limited. Hence, there is a need to apply most recent and powerful optimization techniques to get desired accuracy of optimum solution. In this paper, a recently developed nontraditional optimization technique, particle swarm optimization (PSO) algorithm is presented to find the optimal combination of process parameters of grinding process. 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 algorithm are compared with the previously published results obtained by using other traditional optimization techniques.

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