Korean Journal of Materials Research (한국재료학회지)

Materials Research Society of Korea (한국재료학회)
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Implementation of an Intelligent Video Detection System using Deep Learning in the Manufacturing Process of Tungsten Hexafluoride

딥러닝을 이용한 육불화텅스텐(WF6) 제조 공정의 지능형 영상 감지 시스템 구현

  • Son, Seung-Yong (Graduate School of Industry, Kyungpook National University) ;
  • Kim, Young Mok (Defense Agency for Technology and Quality) ;
  • Choi, Doo-Hyun (School of Electronics Engineering, Kyungpook National University)
  • Received : 2021.10.26
  • Accepted : 2021.12.13
  • Published : 2021.12.27
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Abstract

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 (mAP@0.5 99.4 %) and real-time detection speed (FPS 46).

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References

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