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http://dx.doi.org/10.14775/ksmpe.2021.20.12.001

A Study on Worker Risk Reduction Methods using the Deep Learning Image Processing Technique in the Turning Process  

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.)
Publication Information
Journal of the Korean Society of Manufacturing Process Engineers / v.20, no.12, 2021 , pp. 1-7 More about this Journal
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
Deep Learning; Vision System; Negligent Accident; Convolutional Neural Network(CNN); You Only Look Once(YOLO);
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