DETECTION OF RESISTANCE SPOT WELDING FAULTS IN COPPER MATERIALS BY TRANSFER LEARNING METHOD
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info:eu-repo/semantics/openAccessDate
2023Author
Seker, Halil İbrahimKacar, Sezgin
Castillo, Oscar
Uzun, Suleyman
Pehlivan, Ihsan
Tatli, Zafer
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Abstract
Welding quality is a crucial factor that significantly influences the performance, quality, and strength of products. Therefore, determining weld quality and detecting defects are vital processes in industrial production. Welding various materials such as metal, sheet metal, aluminum, and steel, as well as employing deep learning-based defect detection and classification, are common practices in the industrial field. However, there is a lack of deep learning-based studies analyzing welding faults in the copper-to-copper material welding process. This study focuses on detecting welding faults in resistance spot welding machines using transfer learning networks. The dataset consists of a total of 2531 images, with 582 images labeled as faulty and 1949 images labeled as faultless. The performance results obtained demonstrate classification accuracies ranging between 89% and 100%. Thus, this study presents a solution to an industrial problem faced by the manufacturing company, utilizing a dataset obtained under real production conditions.