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<p>Computer Numerical Control (CNC) inserts are critical components of CNC machine tools, where surface defects can severely compromise machining precision. Traditional manual inspection methods for these defects are inefficient and prone to significant oversight. To address these limitations, this paper presents an automated real-time system for detecting surface defects on inserts. A dedicated dataset of CNC tool inserts was created and annotated with defect categories. We propose an Attention-Augmented Multi-Defect YOLO model (A2MD-YOLO) for surface defect detection on CNC inserts. The development of this model is motivated by key characteristics of the dataset, which include substantial variation in defect sizes, high intra-class appearance variance, and low inter-class variance. A2MD-YOLO achieves higher detection efficiency and accuracy while reducing the rate of missed detections. The A2MD-YOLO model demonstrates a substantial performance improvement, with the <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(mAP_{50-95}\)</EquationSource></InlineEquation> increasing from 0.529 to 0.571 and the missed detection rate decreasing from 21.4% to 11.8%. Finally, the proposed algorithm was implemented into the hardware system, enabling automated detection of surface defects on CNC inserts.</p>

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A machine vision based defect detection method for coated carbide CNC inserts and its industrial automation implementation analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月11日

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  • Junqi Hu,
  • Shi Chen,
  • Sheng Yin,
  • Song Qiu

摘要

Computer Numerical Control (CNC) inserts are critical components of CNC machine tools, where surface defects can severely compromise machining precision. Traditional manual inspection methods for these defects are inefficient and prone to significant oversight. To address these limitations, this paper presents an automated real-time system for detecting surface defects on inserts. A dedicated dataset of CNC tool inserts was created and annotated with defect categories. We propose an Attention-Augmented Multi-Defect YOLO model (A2MD-YOLO) for surface defect detection on CNC inserts. The development of this model is motivated by key characteristics of the dataset, which include substantial variation in defect sizes, high intra-class appearance variance, and low inter-class variance. A2MD-YOLO achieves higher detection efficiency and accuracy while reducing the rate of missed detections. The A2MD-YOLO model demonstrates a substantial performance improvement, with the \(mAP_{50-95}\) increasing from 0.529 to 0.571 and the missed detection rate decreasing from 21.4% to 11.8%. Finally, the proposed algorithm was implemented into the hardware system, enabling automated detection of surface defects on CNC inserts.