화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Korean Journal of Materials Research, Vol.31, No.12, 719-726, December, 2021
DOI10.3740/MRSK.2021.31.12.719  Export Citation
딥러닝을 이용한 육불화텅스텐(WF6) 제조 공정의 지능형 영상 감지 시스템 구현
Implementation of an Intelligent Video Detection System using Deep Learning in the Manufacturing Process of Tungsten Hexafluoride
손승용, 김영목1, 최두현2, †
  • 경북대학교 산업대학원
  • 1국방기술품질원
  • 2경북대학교 전자공학부
Seung-Yong Son, Young Mok Kim1, and Doo-Hyun Choi2, †
  • Graduate School of Industry, Kyungpook National University, Daegu 41566, Republic of Korea
  • 1Defense Agency for Technology and Quality, Daejeon 34327, Republic of Korea
  • 2School of Electronics Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
E-mail:
Through the process of chemical vapor deposition, Tungsten Hexafluoride (WF6) is widely used by the semiconductor industry to form tungsten films. Tungsten Hexafluoride (WF6) is produced through manufacturing processes such as pulverization, wet smelting, calcination and reduction of tungsten ores. The manufacturing process of Tungsten Hexafluoride (WF6) is required thorough quality control to improve productivity. In this paper, a real-time detection system for oxidation defects that occur in the manufacturing process of Tungsten Hexafluoride (WF6) is proposed. The proposed system is implemented by applying YOLOv5 based on Convolutional Neural Network (CNN); it is expected to enable more stable management than existing management, which relies on skilled workers. The implementation method of the proposed system and the results of performance comparison are presented to prove the feasibility of the method for improving the efficiency of the WF6 manufacturing process in this paper. The proposed system applying YOLOv5s, which is the most suitable material in the actual production environment, demonstrates high accuracy ([email protected] 99.4 %) and real-time detection speed (FPS 46).
Keywords:object detection;you only look once (YOLO);tungsten hexafluoride (WF6);reduction;defect detection
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