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A Real-Time Automated Defect Detection System for Ceramic Pieces Manufacturing Process Based on Computer Vision with

Esteban Cumbajin1, Nuno Rodrigues1, Paulo Costa1

  • 1Computer Science and Communications Research Centre, School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, Portugal.

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|January 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an automated defect detection system for ceramic manufacturing using computer vision and deep learning. The developed system achieves high accuracy in identifying defects on ceramic pieces during production.

Keywords:
CNNautomatic surface inspectiondeep learningdefect detectionindustrial surfacequality inspection

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Area of Science:

  • Materials Science
  • Computer Science
  • Industrial Engineering

Background:

  • Automated defect detection is crucial for industrial quality control.
  • Specialized techniques are needed for inspecting unique surfaces like ceramics.
  • Existing methods often lack precision for ceramic anomaly detection.

Purpose of the Study:

  • To propose and develop an advanced defect detection solution for ceramic pieces.
  • To implement a computer vision system with deep learning for real-time industrial application.
  • To enhance quality control in ceramic manufacturing through automated inspection.

Main Methods:

  • Image acquisition and a dedicated labeling platform for dataset creation.
  • Image preprocessing techniques tailored for ceramic surface analysis.
  • Convolutional Neural Networks (CNNs) for real-time defect identification.

Main Results:

  • The system achieved 98.00% accuracy in defect detection.
  • An F1-Score of 97.29% demonstrates the system's effectiveness.
  • Successful implementation in a real-world industrial setting (tableware manufacturing).

Conclusions:

  • The developed automated system significantly improves defect detection on ceramic pieces.
  • The computer vision and deep learning approach offers a precise and effective solution for specialized surfaces.
  • This technology enhances quality control and efficiency in the ceramic manufacturing industry.