Material parameter optimisation of Ohno-Wang kinematic hardening model using multi objective genetic algorithm

Abstract

Ohno-Wang hardening model is an advanced constitutive model to evaluate the cyclic plasticity behaviour of material. This model has capability to simulate uniaxial and biaxial ratcheting response of the material. But, it is required to determine large number of material parameters from several experimental responses in order to simulate this phenomenon. Material parameters for constitutive models are generally determined manually through trial and error approach which is tedious and less accurate. Due to arbitrariness and complexity of cyclic loading, advanced constitutive material models become non-linear and multimodal in functional and parameter space. To overcome this problem, an automated parameter optimisation approach using genetic algorithm has been proposed in the present work to identify Ohno-Wang material parameters of 304LN, stainless steel for uniaxial simulation. Optimisation by this approach has improved the model prediction in uniaxial low cycle and ratcheting fatigue simulations after comparison with the experimental response.