Abstract Scope |
This paper presents a Pareto-based multi-objective machine learning methodology for efficient identification of crystal plasticity model parameters. Specifically, the methodology relays on a Gaussian processes-based surrogate model to limit the number of calls to a given model, and, consequently, to increase the computational efficiency. The constitutive parameters pertaining to an Elasto-Plastic Self-Consistent model including a dislocation density-based hardening law, a backstress law, and a phase transformations law are identified for two alloys, a dual-phase (DP) steel, DP780, subjected to load reversals and a stainless steel (SS), 316L, subjected to strain-rate and temperature sensitive deformation. The optimization objectives were the quasi-static flow stress data for the DP steel case study, while a set of strain-rate and temperature sensitive flow stress and phase volume fraction data for the SS case study. The procedure and results for the two case studies are presented and discussed illustrating advantages and versatility of the methodology. |