Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
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A Study on Worker Risk Reduction Methods using the Deep Learning Image Processing Technique in the Turning Process

선삭공정에서 딥러닝 영상처리 기법을 이용한 작업자 위험 감소 방안 연구

  • Bae, Yong Hwan (Department of Mechanical Education, ANU UNIV.) ;
  • Lee, Young Tae (Department of Electronic Engineering Education, ANU UNIV.) ;
  • Kim, Ho-Chan (Department of Mechanical and Automotive Engineering, ANU UNIV.)
  • 배용환 (안동대학교 기계교육과) ;
  • 이영태 (안동대학교 전자공학교육과) ;
  • 김호찬 (안동대학교 기계자동차공학과)
  • Received : 2021.10.27
  • Accepted : 2021.11.15
  • Published : 2021.12.31
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Abstract

The deep learning image processing technique was used to prevent accidents in lathe work caused by worker negligence. During lathe operation, when the chuck is rotated, it is very dangerous if the operator's hand is near the chuck. However, if the chuck is stopped during operation, it is not dangerous for the operator's hand to be in close proximity to the chuck for workpiece measurement, chip removal or tool change. We used YOLO (You Only Look Once), a deep learning image processing program for object detection and classification. Lathe work images such as hand, chuck rotation and chuck stop are used for learning, object detection and classification. As a result of the experiment, object detection and class classification were performed with a success probability of over 80% at a confidence score 0.5. Thus, we conclude that the artificial intelligence deep learning image processing technique can be effective in preventing incidents resulting from worker negligence in future manufacturing systems.

Keywords

Acknowledgement

이 논문은 2020년도 안동대학교 학술조성연구비 지원에 의하여 연구되었음.

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