Metaheuristic Optimization of Powder Size Distribution in Powder Forming Process Using Multi-Particle Finite Element Method Coupled with Artificial Neural Network and Genetic Algorithm

Abstract

A neural network-based approach is proposed to minimize the maximum axial stress in the powder forming process. The finite element analysis was conducted using a MATLAB code and an ABAQUS python script to generate observations for the neural network training procedure. Powders of three different particle size distributions were mixed, and the mixture fractions were considered as control parameters. The artificial neural network determined the relationship between parameters and objective function. The effect of mixture fractions on maximum axial stress was analyzed. The results showed that the genetic algorithm could effectively determine the optima and the proposed method had strong prediction capability and accuracy.