Efficient parameters identification of a modified GTN model of ductile fracture using machine learning

The ductile fracture behavior of metallic materials is usually coupled with complex stress states. In order to describe the facture behavior of metallic materials under a broad range of stress states, a modified Gurson-Tvergaard-Needleman damage model was proposed by Wei Jiang and has been used to simulate the ductile fracture of 2024-T3 aluminum alloy. However, it was a heavy and trifle job to determine the unknown parameters of the model because of the large number of them and inefficient identification strategy. In this research, we propose an efficient parameters identification strategy of this new damage model based on machine learning algorithm. This strategy combines resilient back-propagation neuro network with genetic algorithm and simulations were implemented in terms of ABAQUS/Explicit. The same strategy of optimization was also used to reduce simulation time. Damage model parameters of 2024-T3 aluminum alloy were identified from the experimental force-displacement curves of the specimens exhibiting high stress triaxiality and negative triaxiality. The identified parameters were verified by using the test results of the specimens providing low and zero stress triaxialities.

Automated summary

An efficient parameter identification strategy for a new damage model was proposed in this research, utilizing a combination of machine learning algorithm including resilient back-propagation neuro network and genetic algorithm, with simulations implemented in terms of ABAQUS/Explicit. The same optimization strategy was used to reduce simulation time.