A cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding

Supervised deep learning has been widely used for weld penetration state classification; however, its performance often degrades significantly under domain shift, such as when transferring models between welding processes with distinct physical mechanisms—for instance, from arc-dominated tungsten inert gas (TIG) welding to keyhole-based laser welding. To overcome this limitation, we propose an unsupervised domain adaptation (UDA) framework integrated with a gradual source domain expansion (GSDE) strategy. Evaluated on dedicated TIG and laser welding datasets, our approach achieves high accuracy in both same-process and cross-process transfer tasks. Specifically, it attains average accuracies of 90.65 % on TIGFH and 90.72 % on LSPS in same-process settings, surpassing a supervised baseline by 35.83 % and 38.87 %, respectively. More notably, in cross-process scenarios, it reaches 80.48 % for TIG → Laser and 81.13 % for Laser→TIG, improving upon the baseline by 43.39 % and 43.40 %. UMAP visualizations verify that the model learns domain-invariant features while maintaining discriminative class boundaries. This method considerably lowers the relabeling cost for new welding processes and enhances the versatility of intelligent monitoring across different welding systems.