The Optimization of the Effective Parameters of the Die in Parallel Tubular Channel Angular Pressing Process by Using Neural Network and Genetic Algorithm Methods

Abstract

One of reasons that researchers in recent years have tried to produce ultrafine grained materials is producing lightweight components with high strength and reliability. There are disparate methods for production of ultra-fine grain materials,one of which is severe plastic deformation method. Severe plastic deformation method comprises different processes, one of which is Parallel tubular channel angular pressing. The aim of this study is optimizing parameters of the noticed process die just by utilizing neural network and genetic algorithm methods that at first for this purpose, by using ABAQUS finite element software, the numerical analysis of the die parameters is performed and the impact of each die parameter on the force of the process and the equivalent strain is examined. Finally, for gaining optimal parameters, MATLAB and neural network optimization methods and genetic algorithm are used. The use of neural network and genetic algorithm illustrated that to achieve the ideal possible situation in order to achieve a flawless super-fine tube, it is imperative to use the friction coefficient of 0.05, tube length of 40 mm, channel angle of 140 degrees and diameter increase difference of 1.5 mm. With such values, strain fluctuations reach 0.23, lowest value, and also the force reaches 0.49 KN and the amount of applied strain reaches its highest value to 2.37.