跨焊接工艺精准预测焊缝深度，无需重新标注数据
A cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding
- 采用无监督域适应+渐进源域扩展，自动对齐不同焊接工艺特征
- 跨工艺预测准确率达80%以上，较传统方法提升超43%
- 适合工业界快速部署智能焊接监测系统
监督式深度学习广泛用于焊缝穿透状态分类，但在不同焊接工艺间迁移时性能显著下降，例如从以电弧主导的钨极惰性气体（TIG）焊接转向基于穿孔的激光焊接。为解决此问题，我们提出一种结合渐进源域扩展（GSDE）策略的无监督域适应（UDA）框架。在专用TIG与激光焊接数据集上评估，该方法在同工艺和跨工艺任务中均表现优异。具体而言，在同工艺设置下，TIGFH和LSPS数据集平均准确率分别达90.65%和90.72%，比监督基线高出35.83%和38.87%。更显著的是，在跨工艺场景中，从TIG到激光及激光到TIG的准确率分别为80.48%和81.13%，较基线提升43.39%和43.40%。UMAP可视化证实模型学得域不变特征且保持类间可区分性。该方法大幅降低新焊接工艺的重新标注成本，提升智能监控在多种焊接系统中的通用性。
原文摘要 · Abstract (English)
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.
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