Welding is commonly used for connecting metal components in these critical metallic infrastructure, such as agricultural facilities, wind turbines, railways, bridges and pipelines. However, welding processes vulnerably lead to forming cracks, pores, and other defects on the surface. These defects not only could result in severer cracks and corrosion, but also may ultimately lead to malfunction and failure of metal components. Inspection of welds is thus critical to ensure the welding quality during fabrication, construction process, and later in-service stage. The visual inspection is the crucial and most cost-effective step to determine if the welding quality is passed or rejected. However, fast and accurately determining welding quality is a challenging task in the conventional visual inspection process, which is highly dependent on the experience and expertise of inspectors, and it is fairly subjective and sometimes even misleading. To meet the gap, we bring machine intelligence to welding visual inspection. Specifically, we developed a low-cost portable embedded device to support advanced machine learning algorithms for real-time welding image processing.
Yifu Gong, Zhibin Lin, Jinhui Wang, Na Gong, "Bringing Machine Intelligence to Welding Visual Inspection: Development of Low-Cost Portable Embedded Device for Welding Quality Control" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision, 2018, pp 279-1 - 279-4, https://doi.org/10.2352/ISSN.2470-1173.2018.09.IRIACV-279