Application of Multi-Objective optimization algorithm and Artificial Neural Networks at machining process

Abstract

Since, experimentally investigation of machining processes is difficult and costly, the problem becomes more difficult if the aim is simultaneously optimization of the machining outputs. This paper presents a novel hybrid method based on the Artificial Neural networks (ANNs), Multi-Objective Optimization (MOO) and Finite Element Method (FEM) for evaluation of thermo-mechanical loads during turning process. After calibrating controllable parameters of simulation by comparison between FE results and experimental results of literature, the results of FE simulation were employed for training neural networks by Genetic algorithm. Finally, the functions implemented by neural networks were considered as objective functions of Non-Dominated Genetic Algorithm (NSGA-II) and optimal non-dominated solution set were determined at the different states of thermo-mechanical loads. Comparison between obtained results of NSGA-II and predicted results of FE simulation showed that, developed hybrid technique of FEM-ANN-MOO in this study provides a robust framework for manufacturing processes.