Translating strain to stress: a single-layer Bi-LSTM approach to predicting stress-strain curves in alloys during hot deformation

Abstract

Abstract This study introduces a novel deep learning network that integrates a single-layer bidirectional long short-term memory (Bi-LSTM) network with a coding layer to analyze the hot deformation behavior of various alloys. The single-layer Bi-LSTM model adeptly predicts experimental stress–strain curves obtained under different deformation temperatures and strain rates, demonstrating superior effectiveness and excellent performance in modeling hot deformation behaviors of the FGH98 nickel-based alloy and TiAl intermetallic alloy. The present model achieves the coefficient of determination of 0.9051 for FGH98 and 0.9307 for TiAl alloys, whereas the corresponding values of 0.8105 and 0.8356 are obtained by the conventional strain-compensated Sellars constitutive equation (SCS model). Additionally, the mean absolute percentage error of the single-layer Bi-LSTM model are 11.37% for FGH98 and 7.16% for TiAl alloys, while the SCS model gains the corresponding error of 15.29% and 17.01%. These results show that the present model has enhances the predictive accuracy exceeding 10% for both FGH98 and TiAl alloys over the SCS model. Consequently, the proposed single-layer Bi-LSTM model provides substantial potential for optimizing manufacturing processes and improving material properties.