Efficient prediction of transient residual stress in directed energy deposition process using temporal convolutional networks

Purpose The study aims to develop a deep learning-based predictive framework for accurately forecasting transient residual stress profiles in the directed energy deposition (DED) process, thereby addressing a critical challenge in metal additive manufacturing. Design/methodology/approach Time-series data generated from experimentally validated finite element simulations were employed to train and evaluate four temporal deep learning models, including LSTM, GRU, Bi-LSTM and temporal convolutional networks (TCN). Findings Among the models tested, the TCN architecture achieved the best performance, with an R2 of 0.99 and a root mean square error (RMSE) of 4.7 MPa. This demonstrates its capability to capture complex temporal variations in residual stress with high fidelity. Research limitations/implications Applicable for single track deposition. Practical implications It can be utilized as an efficient for in situ monitoring of transient residual stress. Social implications Utilized for minimizing computational and experimental fabrication cost. Originality/value The results establish TCN as a robust and scalable approach for real-time residual stress prediction in DED. This advancement provides a pathway for improved process control and optimization, ultimately enhancing the reliability and quality of additively manufactured metal components.