Just another WordPress site - Ruhr-Universität Bochum

Abstract

In classical constitutive modeling, the response of a material to mechanical loads is described by mathematical expressions for the relations between stress and strain or strain-rates. Such mathematical formulations can become rather intricate, e.g., when describing history-dependent plasticity on the level of single-crystalline regions, as it is done in crystal plasticity. Yet, typically, such closed-form constitutive models do not consider microstructural features, as grain size and shape or the crystallographic texture. This situation is rather unsatisfactory from a materials science point-of-view, as it is well-known that such microstructural features do not only control the mechanical behavior of a material but, moreover, they can be subject to change during plastic deformation. This work highlights how microstructure-sensitive data on plastic deformation of polycrystals are generated and used to train numerically efficient machine learning models as constitutive relations that can be applied directly in macroscopic finite-element models of engineering structures. In the first step, we identify suited descriptors for elastic-plastic material behavior of microstructures with different crystallographic textures. In a second step, physics-based micromechanical models are used to generate large data sets for the mechanical behavior of different microstructures under various multi-axial loads. These data serve as basis for the training of machine learning models that can be used to reconstruct and predict flow curves of different materials. Moreover, by virtue of the full tensorial description of the mechanical behavior, these trained machine learning models can be directly used as constitutive rules in finite-element analysis.