Data‐Driven Stress Prediction for Thermoplastic Materials

The present study applies two different machine learning (ML) algorithms to predict the stress‐strain mapping for the non‐linear behaviour of thermoplastic materials: a Long Short‐Term Memory (LSTM) algorithm and a Feed‐Forward Neural Network (FFNN). The approach of this work requires the generation of the stress‐strain curve for specific material parameters. The training data are obtained from the von Mises material law and the Ramberg‐Osgood equation. The four combinations of ML algorithms with constitutive laws are evaluated and show a good agreement with numerical data.