Application of Artificial Neural Networks for Microstructure Models ALFLOW and ALSOFT

In the present work artificial neural networks (ANN) models have been implemented and trained as surrogate models to replicate two physics-based microstructure models for Al-alloys, i.e. the ALFLOW model, which predicts the sub-structure evolution and associated flow stress during plastic deformation and the softening model ALSOFT, which predicts the softening behavior after hot/cold deformation, in view of the combined effect of recovery and recrystallization. Input for both ANN models was limited to variables such as strain, strain rate, time, temperature and solute concentration, and the flow stress as the output. Accuracy and efficiency were tested for different ANN architectures. It is demonstrated that fully connected feed-forward neural network architectures with ∼3 hidden layers are suitable as surrogate models for both ALFLOW and ALSOFT, with a potential speed-up of ∼100x for ALFLOW and ∼10x for ALSOFT.