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<p>The advanced hybrid approach to classify weld bead defects in a Gas Metal Arc Welding (GMAW) process of robotic welding is proposed in this work, contributing to the growing field of intelligent manufacturing and quality control in industrial automation. To precisely categorize six types of weld bead defects such as lack of fusion, burn-through, misalignment, lack of penetration, contamination, and good weld (no defect), a hybrid machine learning approach that combines Convolutional Neural Networks (CNN) and Vision Transformers (ViT) is utilized. The hybrid model leverages the complementary advantages of CNN for the extraction of localized features and ViT for global contextual awareness, resulting in superior classification performance compared to traditional architectures such as ResNet and conventional CNN models. The performance evaluation of proposed model demonstrates the model's robustness and reliability in diverse operational scenarios. The findings highlight the potential of integrating hybrid deep learning models into industrial automation systems to enhance weld bead defect detection to reduce operational inefficiencies and ensure consistent weld quality in manufacturing process.</p>

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Hybrid machine learning models for automated classification of weld defects in gas metal arc robotic welding

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月11日

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  • C. Senthamilarasi,
  • MP. Anbarasi,
  • B. Vinod,
  • K. Senthil Kumar,
  • G. Nagalaxman

摘要

The advanced hybrid approach to classify weld bead defects in a Gas Metal Arc Welding (GMAW) process of robotic welding is proposed in this work, contributing to the growing field of intelligent manufacturing and quality control in industrial automation. To precisely categorize six types of weld bead defects such as lack of fusion, burn-through, misalignment, lack of penetration, contamination, and good weld (no defect), a hybrid machine learning approach that combines Convolutional Neural Networks (CNN) and Vision Transformers (ViT) is utilized. The hybrid model leverages the complementary advantages of CNN for the extraction of localized features and ViT for global contextual awareness, resulting in superior classification performance compared to traditional architectures such as ResNet and conventional CNN models. The performance evaluation of proposed model demonstrates the model's robustness and reliability in diverse operational scenarios. The findings highlight the potential of integrating hybrid deep learning models into industrial automation systems to enhance weld bead defect detection to reduce operational inefficiencies and ensure consistent weld quality in manufacturing process.