Multi Objective Optimization of Machining Parameters in End Milling of AISI1020

In current research, artificial neural network (ANN) and Multi objective genetic algorithm (MOGA) have been used for the prediction multi optimization of end milling operation. Cutting speed, feed rate, depth cut, material density hardness considered as input variables. The predicted values optimized results obtained through ANN MOGA are compared with experimental results. A good correlation has established between an average accuracy 91.983% removal 99.894% tool life, 92.683% machining time, 92.671% tangential cutting force, 92.109% power 90.311% torque. approach proposed to obtain condition each responses. gives 96.801% MRR, 99.653% 86.833% 93.74% 99.473% It concludes that efficiently effectively operation any selected materials before experimental. Implementation these techniques in industries experimentation is useful reduce lead cost consumption also increase productivity product.