Identification and optimization of material constitutive equations using genetic algorithms

The modeling and simulation of engineering materials using constitutive equations requires a large set of optimized coefficients that characterize the hardening or softening behavior. Optimizing this large set of co-efficients with multiple constraints is very challenging using conventional optimization methods. This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The proposed framework demonstrates so-lution convergence, scalability to available data, and high explainability over a wide range of engineering ma-terials, including titanium-based, iron-based, and aluminum-based alloys. The experimental test data for necessary validation of the computational framework was generated using the MTS fatigue testing machine equipped with a highly sensitive extensometer. The Chaboche unified visco-plasticity material model has been implemented as an example in this study which can deal with both cyclic effects, such as combined isotropic and kinematic hardening, and rate-dependent effects associated with visco-plasticity. The experimentally obtained cyclic response of three different classes of materials was compared with their optimized simulated constitutive equations, and the results were in good agreement. Furthermore, the proposed multi-objective optimization using genetic algorithm methodology avoids local optimality and converges to the optimal solution much faster than commercially available software.

Automated summary

This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.