Implementation of an Intelligent Video Detection System using Deep Learning in the Manufacturing Process of Tungsten Hexafluoride

Seung-Yong Son; Young Mok Kim; Doo-Hyun Choi

doi:10.3740/MRSK.2021.31.12.719

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2021 Vol.31, Issue 12 Preview Page Next Page

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

December 2021. pp. 719-726

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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).

Keywords

object detection

you only look once (YOLO)

tungsten hexafluoride (WF₆)

reduction

defect detection

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Information

Publisher :Materials Research Society of Korea
Publisher(Ko) :한국재료학회
Journal Title :Korean Journal of Materials Research
Journal Title(Ko) :한국재료학회지
Volume : 31
No :12
Pages :719-726
Received Date : 2021-10-26
Revised Date : 2021-10-26
Accepted Date : 2021-12-13
DOI :https://doi.org/10.3740/MRSK.2021.31.12.719

Korean Journal of Materials Research ISSN:1225-0562(Print) 2287-7258(Online) 한국재료학회지

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