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<p>Welding defects pose serious threats to the structural integrity and safety of engineering components, potentially leading to equipment failure or even catastrophic accidents. Deep learning technology, with its powerful feature extraction and adaptive learning capabilities, can effectively compensate for the limitations of manual inspection and traditional algorithms in welding defect identification, thereby improving detection accuracy and efficiency. However, applying deep learning to welding defect detection requires interdisciplinary integration, demanding both expertise in algorithm and model design, and in-depth understanding of welding processes, defect mechanisms, and inspection requirements. To address this interdisciplinary challenge, this paper systematically examines the classification of welding methods, types of welding defects and their formation mechanisms. Taking the image acquisition process, image quality, and complexity of welding defects as entry points, it provides an in-depth analysis of the research status and application prospects of deep learning-based welding defect detection technologies. Through comprehensive synthesis of existing research achievements, this study aims to provide theoretical references and practical directions for subsequent research, promote deeper integration between deep learning and welding defect detection, and offer novel approaches for intelligent inspection and evaluation of welding quality.</p>

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Weld Defect Detection: Deep Learning-Based Image Processing and the Mechanisms of Defect Formation

  • 期刊论文

  • 发表日期：

    2025年10月2日

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  • Mengsi Zhang,
  • Meiyan Feng,
  • Changrong Chen,
  • Xinyi Yu,
  • Guofu Lian

摘要

Welding defects pose serious threats to the structural integrity and safety of engineering components, potentially leading to equipment failure or even catastrophic accidents. Deep learning technology, with its powerful feature extraction and adaptive learning capabilities, can effectively compensate for the limitations of manual inspection and traditional algorithms in welding defect identification, thereby improving detection accuracy and efficiency. However, applying deep learning to welding defect detection requires interdisciplinary integration, demanding both expertise in algorithm and model design, and in-depth understanding of welding processes, defect mechanisms, and inspection requirements. To address this interdisciplinary challenge, this paper systematically examines the classification of welding methods, types of welding defects and their formation mechanisms. Taking the image acquisition process, image quality, and complexity of welding defects as entry points, it provides an in-depth analysis of the research status and application prospects of deep learning-based welding defect detection technologies. Through comprehensive synthesis of existing research achievements, this study aims to provide theoretical references and practical directions for subsequent research, promote deeper integration between deep learning and welding defect detection, and offer novel approaches for intelligent inspection and evaluation of welding quality.