Multi Variable Optimization of Surface Grinding Process Using Genetic Algorithm

In the present study, an attempt was made to optimize the ‘Surface Grinding' process by following one of the recent optimizing techniques ‘Genetic Algorithm'. Due to random variation in variable constraints the surface finish is altering in the surface grinding operation which is an adverse effect. So, we tried to get the values for variable constraints, which satisfy both minimization of production cost and maximizations of production rate with the excellent surface finish. For this, we considered variable constraints as workpiece speed, wheel speed, crossfeed rate and down feed rate. To carry out this work we have selected Al2O3 as our grinding wheel, abrasive according to grinding wheel specification ( A 45 K 5 V ). And workpiece as stainless steel because it is resistant to corrosion and staining, low maintenance and familiar luster make it an ideal material for many applications. We used 'C' language compiler as our programming platform. Initially, we performed a surface grinding operation for 6 Stainless steel workpieces with Aluminum Oxide abrasive, by taking the optimum variables from GA, we again performed surface grinding for one more component which gave us better surface finish than previous.