Genetic programming-based symbolic regression (GPSR) is a machine learning method which produces symbolic models that can be readily interpreted. This study utilized GPSR to derive uniaxial texture-based constitutive models for an additively manufactured alloy which were evaluated in post hoc analyses. Training data consisted of microscopy and mechanical testing data provided by the Air Force Research Laboratory (AFRL) which was supplemented using a viscoplastic model calibrated to the observed data. The validity of the calibrated crystal plasticity viscoplastic model is demonstrated as part of the 2019 AFRL Additive Manufacturing Modeling Challenge Series. Additionally, an expression evaluator was developed to integrate the constitutive models into the topology optimization software package Plato. A significant aspect of this paper is the presentation of these topics as components within a highly automated framework that allows efficient incorporation of microstructural characteristics into design activities. A topology optimization example was conducted using the GPSR results that constitutes application of the automated framework and post hoc analyses of the GPSR models demonstrate interpretability, suitability, and a probabilistic method to quantify domain bounds.

基于遗传编程的符号回归 (GPSR) 是一种机器学习方法，可生成易于解释的符号模型。本研究利用 GPSR 为增材制造合金推导出基于单轴织构的本构模型，并在事后分析中对其进行评估。训练数据包括由空军研究实验室 (AFRL) 提供的显微镜和机械测试数据，并使用根据观察数据校准的粘塑性模型进行补充。作为 2019 年 AFRL 增材制造建模挑战系列的一部分，证明了校准晶体塑性粘塑性模型的有效性。此外，还开发了一个表达式评估器以将本构模型集成到拓扑优化软件包 Plato 中。本文的一个重要方面是将这些主题作为高度自动化框架中的组件进行呈现，该框架允许将微观结构特征有效地结合到设计活动中。使用 GPSR 结果进行拓扑优化示例，该示例构成自动化框架的应用，GPSR 模型的事后分析证明了可解释性、适用性和量化域边界的概率方法。