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This work addresses a modeling framework for identification of material parameters based on multi-objective optimization aiming at multiple mechanical tests or experimental data sets. The multi-objective problem is described within the Pareto theory, from which a strategy based on the global method solved in a normalized feasible objective space is proposed. The method maps the objective space onto a new normalized space where the optimal solution is found by minimizing a global error function. The strategy is illustrated by identification of hardening and damage parameters based on tensile and compression tests featuring the effects of tensile-dominant and compressive-dominant stress states.

A multi-objective framework for identification of material parameters based on multiple mechanical tests