Ductile Fracture Prediction for Ti6Al4V Alloy Based on the Shear-Modified GTN Model and Machine Learning

To investigate the failure behavior of Ti6Al4V alloy under complex stress states, this study designed tensile specimens with different notches to achieve high, medium, and low stress triaxiality conditions. By adjusting the width of the notch spacing of the specimens, the failure mode can be transformed from tension-dominated fracture to shear stress-dominated fracture, which enables further examination of the damage model’s effectiveness. A shear-modified Gurson–Tvergaard–Needleman (GTN) model was employed to predict the failure behavior under various stress states. For calibrating the GTN parameters, a machine learning approach was adopted. Back propagation (BP) neural networks were used to construct surrogate models for predicting the fracture strains of three typical specimens, and genetic algorithms (GAs) were integrated for optimization, to minimize the discrepancy in fracture strains between experimental results and finite element analysis (FEA). Finally, an optimal set of parameters was determined. This set of parameters can effectively predict the failure behavior of all specimens, including not only the stress–strain curves, but also the failure modes (fracture locations).