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.