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An automatic sorting system for electronic components detached from waste printed circuit boards.

Yingqi Lu1, Bo Yang1, Yichun Gao1

  • 1School of Environmental Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, People's Republic of China.

Waste Management (New York, N.Y.)
|October 26, 2021
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Summary

Recycling electronic components (ECs) from waste printed circuit boards (PCBs) is crucial for resource recovery and pollution reduction. This study introduces an automated system using YOLO-V3 for accurate EC classification, improving hazardous e-waste recycling efficiency.

Keywords:
AutomationObject detectionRecyclingWaste electrical and electronic equipment

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Area of Science:

  • Materials Science
  • Environmental Science
  • Computer Science

Background:

  • Electronic waste (e-waste) recycling is vital for environmental protection and resource recovery.
  • Printed circuit boards (PCBs) are significant e-waste components containing valuable and hazardous materials.
  • Manual sorting of electronic components (ECs) from PCBs is inefficient and poses health risks due to hazardous environments.

Purpose of the Study:

  • To develop an automated system for classifying and sorting electronic components (ECs) from waste printed circuit boards (WPCBs).
  • To improve the efficiency and safety of e-waste recycling processes.
  • To achieve accurate material recovery from WPCBs.

Main Methods:

  • Utilized the YOLO-V3 object detection algorithm trained on a custom dataset for EC classification.
  • Developed an improved desoldering process using a nitrogen atmosphere to protect EC surfaces during detachment.
  • Proposed a smart on-line sorting system for automated separation of detached ECs.

Main Results:

  • The YOLO-V3 model demonstrated satisfactory classification accuracy for various EC categories.
  • The nitrogen atmosphere during desoldering effectively protected EC surfaces, preserving detection accuracy.
  • The proposed system enables automatic separation of ECs from WPCBs.

Conclusions:

  • Automated classification and sorting of ECs using YOLO-V3 are feasible and effective for WPCB recycling.
  • The developed system enhances recycling efficiency and worker safety by minimizing manual handling.
  • This approach contributes to sustainable e-waste management and critical material recovery.