Ductile damage parameters identification for cold metal forming applications.

Ductile damage mechanics is essential to predict failure during cold metal forming applications. Several damage models can be found in the literature. These damage models are coupled with the mechanical behavior so as to model the progressive softening of the material due to damage growth. However, the identification of damage parameters remains an issue. In this paper, an inverse analysis approach is set-up to identify ductile damage parameters, based on different kind of mechanical tests and observables. The Lemaitre damage model is used and damage is coupled with the material behavior. The Efficient Global Optimization (EGO) method is used in a parallel environment. This global algorithm based on kriging meta-model enables the identification of a set of damage parameters based on experimental observables. Global and local observables are used to identify these parameters and a special attention is paid to the computation of the cost function. Finally, an identification procedure based on displacement field measurements is presented and applied for damage parameters identification. [ABSTRACT FROM AUTHOR]