用符号回归自动发现两种合金的塑性变形模型。
Identification of Empirical Constitutive Models for Age-Hardenable Aluminium Alloy and High-Chromium Martensitic Steel Using Symbolic Regression
- 通过符号回归从实验数据中自动推导材料本构方程。
- 成功构建铝合金与高铬马氏体钢在拉压下的应力-应变模型。
- 适合材料建模与智能制造领域研究人员参考。
工艺-结构-性能关系是材料科学与工程的基础，对新材料开发至关重要。符号回归是一种强大工具，可自动发现描述这些关系的数学模型，从而预测特定制造条件下材料的行为，并优化强度、弹性等性能。本文展示如何利用符号回归推导金属合金在塑性变形过程中的本构模型。本构建模是描述不同加载条件下材料应力与应变关系的数学框架。研究选取两种材料（时效强化铝合金与高铬马氏体钢）及两种测试方法（压缩与拉伸），获取所需应力-应变数据。结果表明符号回归在建模中的优势，同时讨论了潜在挑战。
原文摘要 · Abstract (English)
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.
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