Integration of finite element simulation and intelligent methods for evaluation of thermo-mechanical loads during hard turning process

Abstract

The machined surfaces are mainly affected by thermo-mechanical loads during machining processes. In this regard, thermal loads increase tensile residual stress and heat-affected zone; however, mechanical loads increase fatigue strength and compressive residual stress on the machined workpiece during the process. Since experimental investigation is difficult, the problem becomes more difficult if the aim is minimizing thermal loads, while maximizing mechanical loads during the hard turning process. This article presents a hybrid method based on the artificial neural networks, multiobjective optimization, and finite element analysis for evaluation of thermo-mechanical loads during the orthogonal turning of AISI H13-hardened die steel (52HRC). First, using an iterative procedure, controllable parameters of simulation (including contact conditions and flow stress) are determined by comparison between finite element and experimental results from the literature. Then, the results of finite element simulation at the different cutting conditions and tool geometries were employed for training neural networks by genetic algorithm. Finally, the functions implemented by neural networks were considered as objective functions of nondominated genetic algorithm and optimal nondominated solution set were determined at the different states of thermal loads (workpiece temperature) and mechanical loads (workpiece effective strain). Comparison between the obtained results of nondominated genetic algorithm and predicted results of finite element simulation showed that the hybrid technique of finite element method–artificial neural networks–multiobjective optimization provides a robust framework for machining simulation of AISI H13.