Microstructure-property mapping modeling for AZ31 alloy rolling deformation using improved PSO-BP neural network

Abstract

The mechanical properties of wrought magnesium alloys depend on their microstructure, particularly the grain size and its distribution. Conventional modeling methods struggle to accurately predict the complex physical relationship between them. In the present study, a microstructure-property mapping model based on artificial neural network was proposed to accurately predict the micro-Vickers hardness, 0.2% proof stress and tensile strength of rolled as-casting AZ31 alloy. This method considered the average grain size and its dispersion as the main influencing factors. Firstly, the diversified data of microstructure and mechanical properties of cast-rolling AZ31 alloy was obtained through multi-process and different cross rolling experiments at different initial rolling temperatures. Considering the promoting effect of nonlinear inertia weight and genetic operators on the convergence and search ability, the particle swarm optimization (PSO) algorithm was improved and subsequently introduced into the back propagation (BP) neural network to pre-training the initial weights and biases. Finally, a microstructure-mechanical property mapping model based on the improved PSO-BP neural network was constructed and verified in detail. Results show that the combination of nonlinear inertia weight and selection and crossover process of genetic algorithm can significantly improve the population diversity and convergence performance of PSO algorithm. This can optimize the initial weights and biases required for the operation of BP neural network model. The optimization ability of the model is remarkably improved in the process of establishing the microstructure-property mapping relationship.

Keywords

• AZ31 magnesium alloy
• Rolling
• Particle swarm optimization
• Back propagation neural network
• Microstructure-property mapping modeling