한국생산제조학회 학술지 영문 홈페이지
[ Special Issue : SW-based smart-Factory Operation Managements(FOMs) Technology ]
Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 32, No. 3, pp.182-188
ISSN: 2508-5107 (Online)
Print publication date 15 Jun 2023
Received 05 Apr 2023 Revised 16 May 2023 Accepted 22 May 2023
DOI: https://doi.org/10.7735/ksmte.2023.32.3.182

대형 챔버 최종 가공 공정 데이터 기반의 시뮬레이션과 FOM을 통한 생산성 개선 예측

오상석a ; 장선준b ; 김수영a, *
Prediction of Productivity Improvement Applying Simulation and FOM based on Final Machining Process Data of Large Chamber
Sang Suk Oha ; Seon-Jun Jangb ; Su Young Kima, *
aDepartment of AI Smart Factory Convergence Engineering, Hoseo University
bDivision of Mechanical and Automotive Engineering, Hoseo University

Correspondence to: *Tel.: +82-41-540-9960 E-mail address: df2030@hoseo.edu (Su Young Kim).

Abstract

Because of the acceleration of DX and the influence of the Fourth Industrial Revolution, productivity improvement is required for manufacturing companies to increase efficiency, reduce costs, and rapidly adapt to changing market conditions. Therefore, efficient evaluation and verification are possible if the simulation of the FOM(smart-factory operation management) and CPS(cyber-physical system) -based 3D process optimization model is analyzed by field manufacturing data as a method of predicting the effect of the improvement plan. Therefore, we analyze the process data of a large chamber using FOM, and sample the data, we determine the effect of enhancement through a simulation model, and predict the effectiveness of productivity improvement by feedback and verification. Consequently, many small and medium-sized manufacturing companies can achieve optimization of data-driven smart manufacturing sites and improve profitability through loss factor analysis and production forecasting, creating a foundation for growth as a competitive enterprise.

Keywords:

FOM(smart-factory operation management), CPS(cyber physical system), 3D simulation, Productivity improvement, Prediction

Acknowledgments

이 논문은 중소벤처기업부 ‘중소기업연구인력지원사업’의 재원으로 한국산학엽협회(AURI)의 지원을 받아 수행된 연구임. (2023년 기업연계형연구개발인력양성사업, 과제번호 : RS-2023-00259258).

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Sang Suk Oh

Graduate Student in Department of AI Smart Factory Convergence Engineering, Hoseo University. His research interest is Smart Factory Operation Management with AI.

E-mail: sangsoh@naver.com

Seon Jun Jang

Associate Professor in Division of Mechanical and Automotive Engineering, Hoseo University. His research interest is Vibrational Energy Harvesters and Wave Energy Converters.

E-mail: mweagle@hoseo.edu

Su Young Kim

Professor in Department of AI Smart Factory Convergence Engineering, Hoseo University. His research interest is Applications of FOMs (smart-Factory Operation Managements).

E-mail: df2030@hoseo.edu