Lukács Hunor István, Beregi Bence Zsolt, Porteleki Balázs, Fischl Tamás, Botzheim János
Department of Artificial Intelligence, Faculty of Informatics, ELTE Eötvös Loránd University, Pázmány P. Sétány 1/A, Budapest, 1117, Hungary.
Robert Bosch Kft, Gyömrői út 104, Budapest, 1103, Hungary.
Sci Rep. 2025 May 1;15(1):15276. doi: 10.1038/s41598-025-00257-2.
In industrial processes, quality assurance through methods such as visual inspection is essential for ensuring process stability. Traditional manual visual inspection is a time-consuming and costly endeavor. If the opportunity arises, replacing manual visual inspection with AI could lead to significant efficiency gains. However, simply judging the correctness or incorrectness of a process is often insufficient; quantitative attributes must also be associated with visual inspection. This paper proposes a solution for replacing manual visual inspection with AI specifically for welded joints. The aim is not only to detect the presence of weld joints but also to assess their geometric dimensions. Leveraging a proposed Attention U-Net architecture in combination with rule-based metrics, the proposed method offers a novel solution for identifying welding lines in images. By integrating semantic segmentation techniques, the method effectively distinguishes weld joint elements, while rule-based metrics facilitate the identification of critical cases requiring human intervention. Experimental results demonstrate the method's capability to automate a significant portion of inspection tasks, thereby reducing the reliance on manual labor and enhancing overall process efficiency and reliability.
在工业生产过程中，通过诸如目视检查等方法进行质量保证对于确保过程稳定性至关重要。传统的人工目视检查既耗时又成本高昂。如果有机会，用人工智能取代人工目视检查可大幅提高效率。然而，仅仅判断一个过程的正确与否往往是不够的；定量属性也必须与目视检查相关联。本文提出了一种用人工智能专门取代焊接接头人工目视检查的解决方案。其目的不仅是检测焊接接头的存在，还要评估其几何尺寸。利用所提出的注意力U-Net架构并结合基于规则的指标，该方法为识别图像中的焊接线提供了一种新颖的解决方案。通过集成语义分割技术，该方法有效地区分了焊接接头元素，而基于规则的指标则有助于识别需要人工干预的关键情况。实验结果证明了该方法能够自动完成大部分检查任务，从而减少对人工的依赖，提高整体过程效率和可靠性。
