Identification of Empirical Constitutive Models for Age-Hardenable Aluminium Alloy and High-Chromium Martensitic Steel Using Symbolic Regression
Evgeniya Kabliman and Gabriel Kronberger
过程结构-属性关系是材料科学和工程的基础,是开发新的和改进的材料的关键。 符号回归是揭示描述这些关系的数学模型的有力工具。 它可以自动生成方程来预测特定制造条件下的材料行为,并优化强度和弹性等性能特性。 本作品说明了符号回归如何导出描述各种金属合金在塑性变形过程中行为的构成模型。 组织建模是一个数学框架,用于理解不同加载条件下材料中应力和应变之间的关系。 在这项研究中,两种材料(可老化的铝合金和高铬马氏体钢)和两种不同的测试方法(压缩和张力)被认为是获得所需的应力应变数据。 结果强调了使用符号回归的好处,同时也讨论了潜在的挑战。
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 re...