Predicting stress–strain curves using transfer learning: Knowledge transfer across polymer composites

Abstract

The engineering stress–strain curve of a material allows one to determine mechanical properties such as elastic modulus, strength, and toughness. While machine learning has recently been used to predict stress–strain curves, large volumes of experimental data are required to achieve high prediction accuracy. More importantly, conventional machine learning models are not generalizable from one material to another. To address this issue, a novel transfer learning approach is introduced to predict stress–strain curves across different fiber reinforced polymer (FRP) composites fabricated via additive manufacturing. Optimal transport (OT) is integrated with the transfer learning approach by mapping the source label space to the target label space, which further improves knowledge transferability across different FRP composites. The stress–strain curves of the additively manufactured FRP composites are generated under flexural loading conditions. Experimental results show that the OT-integrated transfer learning approach can predict the complex stress–strain curves of FRP composites with small training data. The predictive model achieved a mean absolute percentage error (MAPE) of less than 10%. The extracted modulus and strength from the predicted stress–strain curves achieved MAPEs of less than 5% and 10%, respectively. This work demonstrates that transfer learning has the potential to transform how stress–strain curves are generated.