Porous single crystals under triaxial creep loadings: A data-driven modelling approach

The evolution of porosity plays an important role during failure processes in metallic materials at room and elevated temperatures. Recent experiments showed that void growth and coales-cence may lead to the creep rupture of single crystal materials, mainly through plastic flow. It is highly challenging to accurately model the combined effects of multiple controlling factors, such as crystal orientation, matrix hardening behaviour, void shape and stress state. In this work, a neural network surrogate model was constructed to predict the behaviour of single crystals containing voids under creep loadings. Training of the neural network model was performed using 3D micromechanical finite element simulation results. A detailed assessment was carried out to examine the interpolation ability of the neural network model with respect to the crystal orientation, stress triaxiality and Lode parameter, as well as its extrapolation ability. Within the interpolation range, the neural network predictions of the deformation behaviour and porosity evolution of porous single crystals agree well with the finite element results of test dataset. The characteristic quantities, including the effective steady state strain rate, critical time and effective strain for the onset of tertiary creep, were predicted within 20% error with respect to the micromechanical simulation results for most of cases considered. Finally, the extrapolation ability of the model in predicting deformation behaviour of porous single crystal is good for the stress triaxiality lower than 1/3. However, the prediction of deformation and void evolution is deteriorated for the stress triaxiality larger than the range of value used for the training.