跨进程焊接渗透状态预测:基于无监督域适应的方法

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

精选理由

想跨焊接工艺直接套用模型?这篇论文用无监督域适应,在TIG和激光焊接上准确率从四成多拉到八成以上,不用重新标数据。

AI 摘要

该论文提出一种无监督域适应框架结合渐进源域扩展策略,用于TIG焊接与激光焊接之间的跨进程渗透状态分类。在TIGFH数据集上达到90.65%准确率,在LSPS数据集上达到90.72%,分别超过监督基线35.83%和38.87%。跨进程任务中,TIG到激光准确率80.48%,激光到TIG准确率81.13%,分别提升43.39%和43.40%。方法无需目标域标注,显著降低新焊接工艺的重新标注成本。

AI 翻译 · 中文

该论文提出一种无监督域适应框架结合渐进源域扩展策略,用于TIG焊接与激光焊接之间的跨进程渗透状态分类。在TIGFH数据集上达到90.65%准确率,在LSPS数据集上达到90.72%,分别超过监督基线35.83%和38.87%。跨进程任务中,TIG到激光准确率80.48%,激光到TIG准确率81.13%,分别提升43.39%和43.40%。方法无需目标域标注,显著降低新焊接工艺的重新标注成本。

arXiv cs.AISupervised 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 processe