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Utilizing Deep Learning for Defect Inspection in Hand Tool Assembly.

Hong-Dar Lin1, Cheng-Kai Jheng1, Chou-Hsien Lin2

  • 1Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an automated visual inspection system for detecting assembly defects in hand tools. The system effectively identifies missing, misplaced, foreign, or extra parts, improving product quality and safety.

Keywords:
R-CNN series modelsassembly defectsdeep learninghand toolsvisual inspection

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Area of Science:

  • Manufacturing Engineering
  • Computer Vision
  • Quality Control

Background:

  • Product assembly integrity is crucial in precision industries, yet manual oversight leads to defects.
  • Common assembly anomalies like missing, misplaced, foreign, or extra parts cause significant customer complaints, especially in the hand tool industry.
  • Current inspection methods rely on manual labor and experience, lacking comprehensive defect detection capabilities.

Purpose of the Study:

  • To propose and evaluate an automated visual inspection system for detecting assembly defects in hand tool manufacturing.
  • To address the limitations of manual inspection by providing a systematic approach to identify common assembly anomalies.
  • To investigate the performance of deep learning models, specifically the R-CNN series, for automated defect classification.

Main Methods:

  • Collected images from three assembly stations of the ratchet wrench process, documenting 28 defect types across four anomaly categories.
  • Preprocessed images by filtering noise and extracting Region of Interest (ROI) using a circular mask.
  • Utilized manual annotation for defect labeling and applied R-CNN based models for feature extraction and classification, comparing their performance against other object detection models.

Main Results:

  • The developed automated visual inspection system demonstrated high effectiveness in detecting and classifying assembly defects.
  • The best-performing model at each station achieved an average defect detection rate (1-β) of 92.64%.
  • The system achieved an average misjudgment rate (α) of 6.68% and an average correct classification rate (CR) of 88.03%.

Conclusions:

  • The automated visual inspection system successfully addresses the need for comprehensive defect detection in hand tool assembly.
  • Implementing the best-suited deep learning model at each assembly station significantly enhances defect inspection accuracy and efficiency.
  • This technology offers a robust solution to reduce customer complaints and improve overall product quality and safety.