WEB Data-Oriented Constitutive Modeling for Plasticity in MetalsFriday (25.09.2020) 12:05 - 12:20 M: Modelling and Simulation 2 Part of:
Constitutive models for plastic deformation of metals are commonly formulated as yield functions and flow rules that determine the transition from elastic to plastic response of a material to applied mechanical loads. The yield functions are based on the equivalent stress as measure for the applied load and the yield strength as material property. In this work, a novel mathematical formulation is developed that allows the efficient use of machine learning algorithms to describe the elastic-plastic deformation of a solid under arbitrary mechanical loads and that can replace the standard flow rules with more flexible algorithms. By exploiting basic physical principles of elastic-plastic deformation, the dimensionality of the problem is reduced without loss of generality. The data-oriented approach inherently offers a great flexibility to handle different kinds of material anisotropy without the need for explicitly calculating a large number of model parameters. The applicability of this formulation in finite-element simulations is demonstrated and the results are compared to formulations based on Hill-like anisotropic plasticity as standard model. The method is furthermore applied to data resulting from physics-based micromechanical simulations of the mechanical behavior of polycrystals with varying crystallographic texture. This demonstrates the power of the data-oriented constitutive model, in which microstructural parameters, here in form of texture parameters, can explicitly be used as input features of the flow rule. In this way, data-oriented constitutive modeling will enable new kinds of material-specific simulations and also provide a new way to homogenize numerical results in a scale-bridging approach.