19c96fd517d854497e8-292e 20220211045732044 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 06 30 2021 10 8 Ph.D Scholar, Department of Mechanical Engineering, Gujarat Technological University, Ahmedabad (Gujarat), India. Jignesh G Parmar Dr. Komal G Dave Professor, Department of Mechanical Engineering, Lalbhai Dalpatbhai College of Engineering, Ahmedabad (Gujarat), India. In current research, artificial neural network (ANN) and Multi objective genetic algorithm (MOGA) have been used for the prediction and multi objective optimization of the end milling operation. Cutting speed, feed rate, depth of cut, material density and hardness have been considered as input variables. The predicted values and optimized results obtained through ANN and MOGA are compared with experimental results. A good correlation has been established between the ANN predicted values and experimental results with an average accuracy of 91.983% for material removal rate, 99.894% for tool life, 92.683% for machining time, 92.671% for tangential cutting force, 92.109% for power and 90.311% for torque. The MOGA approach has been proposed to obtain the cutting condition for optimization of each responses. The MOGA gives average accuracy of 96.801% for MRR, 99.653% for tool life, 86.833% for machining time, 93.74% for cutting force, 93.74% for power and 99.473% for torque. It concludes that ANN and MOGA are efficiently and effectively used for prediction and multi objective optimization of end milling operation for any selected materials before the experimental. Implementation of these techniques in industries before the experimentation is useful to reduce the lead time, experimental cost and power consumption also increase the productivity of the product. 06 30 2021 54 63 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.H9225.0610821 https://www.ijitee.org/portfolio-item/H92250610821/