Identification of Empirical Constitutive Models for Age-Hardenable Aluminium Alloy and High-Chromium Martensitic Steel Using Symbolic Regression

TL;DR

This paper demonstrates how symbolic regression can derive empirical constitutive models for aluminium alloy and steel, capturing their stress-strain behavior during deformation to aid material development.

Contribution

It introduces a method for automatically generating mathematical models of material behavior using symbolic regression for specific alloys and testing conditions.

Findings

Successfully derived constitutive models for aluminium alloy and steel.

Showed symbolic regression's effectiveness in modeling stress-strain relationships.

Discussed challenges and benefits of using symbolic regression in materials modeling.

Abstract

Process-structure-property relationships are fundamental in materials science and engineering and are key to the development of new and improved materials. Symbolic regression serves as a powerful tool for uncovering mathematical models that describe these relationships. It can automatically generate equations to predict material behaviour under specific manufacturing conditions and optimize performance characteristics such as strength and elasticity. The present work illustrates how symbolic regression can derive constitutive models that describe the behaviour of various metallic alloys during plastic deformation. Constitutive modelling is a mathematical framework for understanding the relationship between stress and strain in materials under different loading conditions. In this study, two materials (age-hardenable aluminium alloy and high-chromium martensitic steel) and two different testing methods (compression and tension) are considered to obtain the required stress-strain data. The results highlight the benefits of using symbolic regression while also discussing potential challenges.