Scholarlink

The quality of the entire mechanical system is directly impacted by the quality of metal gears, an essential transmission component. As such, detecting defects on the end face of metal gears accurately holds significant practical importance with the advancement of industrial automation. Traditional methods for defect detection often involve manual visual inspection or basic image processing techniques, leading to inefficiencies and inaccuracies. In this study, we introduce a novel model for detecting defects on metal gear endfaces utilizing deep learning technology. Through comparison with current mainstream algorithms, our model demonstrated superior performance in terms of accuracy and efficiency, highlighting the vast potential of deep learning in enhancing metal gear endface defect detection.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep Learning Model for Metal Gear Defect Detection

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Shuai Yang,
  • Chen Wang,
  • Lin Zhou

摘要

The quality of the entire mechanical system is directly impacted by the quality of metal gears, an essential transmission component. As such, detecting defects on the end face of metal gears accurately holds significant practical importance with the advancement of industrial automation. Traditional methods for defect detection often involve manual visual inspection or basic image processing techniques, leading to inefficiencies and inaccuracies. In this study, we introduce a novel model for detecting defects on metal gear endfaces utilizing deep learning technology. Through comparison with current mainstream algorithms, our model demonstrated superior performance in terms of accuracy and efficiency, highlighting the vast potential of deep learning in enhancing metal gear endface defect detection.