ArXiv

Same-process accuracy: 90.65% on TIGFH and 90.72% on LSPS using the proposed…

2026-06-24 · 17:52 UTC ·Sen Li, Haichao Cui, Chendong Shao... ·1 min read
Authors
Sen Li, Haichao Cui, Chendong Shao...
Categories
cs.CV, cs.AI
arXiv
https://arxiv.org/abs/2606.26078v1
PDF
https://arxiv.org/pdf/2606.26078v1

Brief

Unsupervised domain adaptation with a gradual source domain expansion (GSDE) strategy is proposed to predict weld penetration states across TIG and keyhole laser welding. Evaluated on TIGFH and LSPS datasets, the method achieves ~90.7% same-process accuracy and ~80.5% cross-process transfer accuracy, substantially outperforming supervised baselines and producing domain-invariant features per UMAP visualizations.

Why it matters

Same-process accuracy: 90.65% on TIGFH and 90.72% on LSPS using the proposed UDA+GSDE method, outperforming a supervised baseline by 35.83% and 38.87%, respectively.

Key details

  • Cross-process transfer: 80.48% accuracy for TIG→Laser and 81.13% for Laser→TIG, improving over the baseline by 43.39% and 43.40%.
  • Method and validation: an unsupervised domain adaptation framework with gradual source domain expansion (GSDE) learns domain-invariant, class-discriminative features (verified by UMAP) and reduces relabeling cost for new welding processes.
Source evidence

Abstract

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 to Laser and 81.13% for Laser to 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.