Knowledge-enabled stress–strain prediction in additive manufacturing via transferable deep temporal modelling

Stress–strain curves are essential for understanding the mechanical behaviour of materials, particularly in additive manufacturing. However, achieving accurate predictions for material extrusion (MEX) printed components often demands extensive experimental data, while traditional machine learning models struggle to generalise across diverse printing parameters. Here, we introduce a novel long short-term memory and temporal convolutional network with transfer learning (LSTM-TCN-TL) framework that enables accurate stress–strain predictions under varying process conditions using minimal data. Our approach employs transfer learning, adapting models trained on stress–strain data for samples printed at specific angles to predict behaviour across different infill densities. By leveraging transfer learning, the number of required specimens per process parameter is reduced from five to one, achieving an 80 % reduction in experimental workload while maintaining high predictive accuracy. Experimental results demonstrate that the LSTM-TCN model achieves high predictive accuracy on the original dataset (R 2 = 0.94, RMSE = 0.07) and maintains robust performance following transfer learning (R 2 = 0.81, RMSE = 0.09), even when dataset size is significantly reduced, and input variables change from angles to densities. The predicted stress–strain curves closely align with experimental results, highlighting the framework’s generalisability and efficiency. This study provides a scalable and data-efficient solution for stress–strain prediction, reducing data requirements while enhancing model applicability across varying MEX process parameters, thereby advancing predictive modelling capabilities in additive manufacturing.