Process-informed Spatial Conditioning within Data-driven Framework for High-fidelity Reconstruction of Residual Stress and Distortion Fields in Wire Arc Additive Manufacturing

Residual stress and distortion in wire arc additive manufacturing (WAAM) severely compromise dimensional accuracy and service performance of components. Existing deep-learning models mainly consider geometry and deposition paths while insufficiently accounting for process parameters, limiting prediction accuracy and adaptability across varying conditions. To address this issue, a Process-Conditioned Spatial Modulation Y-Net (PCSM-YNet) multimodal deep learning framework is proposed for high-fidelity reconstruction of residual stress and distortion fields. A Process-Conditioned Residual Spatial Modulation (PCRSM) module couples process-parameter mappings with path-sensitive weights to modulate deep features; a Thermo-Mechanical Response-Guided Cross-Level Feature Fusion (TMR-CFF) module selectively reorganizes encoder features during decoding; and a Process-Conditioned Spatial Reweighting (PCSR) module enhances critical local responses in skip connections. PCSM-YNet achieves R² above 0.9 for all prediction tasks and captures stress–distortion trends under varying heat-source powers and deposition speeds. Compared with 3D U-Net and 3D ResUNet, this model maintains more consistent predictive accuracy across varying process conditions, reducing the maximum RMSE of Y-direction distortion by approximately 25% and 16%, respectively, and the peak absolute error of residual von Mises stress by up to approximately 25% and 20%. The framework reduces reliance on repeated experiments and costly simulations, supporting rapid WAAM process evaluation, optimization, and quality control.