A Novel Approach for Modeling Strain Hardening in Plasticity and Its Material Parameter Identification by Bayesian Optimization for Automotive Structural Steels Application

Constitutive modeling in plasticity is a critical topic in solid mechanics. However, modeling nonlinear plasticity remains a challenge due to the theoretical complexity in representing realistic material behavior. This work aims to develop a general material model based on a rational polynomial function for plasticity and to use Bayesian optimization to identify its parameters. As a data-driven approach, Bayesian optimization effectively estimates model parameters for a high-computational-cost model. In this work, automotive structural steel is selected as a representative example to benchmark the proposed approach. Our results demonstrate that the rational polynomial function effectively models the plasticity behavior for metallic alloy before the failure point, and Bayesian optimization successfully estimates the parameters. This novel framework also has the potential to be applied to other materials, whose constitutive models can be defined by stress–strain curves.