[ Special Issue : Smart Manufacturing Innovation based on FOMs ]

Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 31, No. 3, pp.211-215

ISSN: 2508-5107 (Online)

Print publication date 15 Jun 2022

Received 14 May 2022 Revised 08 Jun 2022 Accepted 09 Jun 2022

DOI: https://doi.org/10.7735/ksmte.2022.31.3.211

4M 데이터 기반 FOM분석을 통한 대형 진공 챔버 가공공정의 리드타임 단축

이남은^a ; 오상석^a ; 배병성^a ; 김수영^a^{, *}

Lead-Time Reduction of Machining Process Using FOM Analysis Based on 4M Data of Large Vacuum Chamber

Nam Eun Lee^a ; Sang Suk Oh^a ; Byung Seong Bae^a ; Su Young Kim^a^{, *}

aDepartment of AI Smart Factory Convergence Engineering, Hoseo University

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

Abstract

Digital transformation of small- and medium-sized enterprises is required to improve quality and productivity by collection and analysis of manufacturing data, and satisfaction of delivery dates for competitiveness. For example, a smart-factory operation management solution was performed by analyzing data to reduce the lead time for each process and efficiently operate facilities in manufacturing a small made-to-order large vacuum chamber. For each factor, the non-operation time , and an improvement effect was predicted. Thus, many companies that manufacture a small quantity can achieve process optimization of made-to-order product and develop a foundation for smart factory operations and productivity improvements based on data.

Keywords:

FOM(smart-factory operation management), 4M data analysis, Lead-time, Machining innovation, Vacuum chamber

Acknowledgments

이 성과물은 중소벤처기업부 ‘중소기업연구인력지원사업’의 재원으로 한국산학엽협회(AURI)의 지원을 받아 수행된 연구임. (2022년 기업연계형연구개발인력양성사업, 과제번호: S3282285)

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Nam Eun Lee

Graduate student in Department of AI Smart Factory Convergence Engineering, Hoseo University. His research interest is FOM (smart-Factory Operation Management) with AI.

E-mail: leenameun00@naver.com

Sang Suk Oh

Graduate student in Department of AI Smart Factory Convergence Engineering, Hoseo University. His research interest is FOM (smart-Factory Operation Management) with AI.

E-mail: sangsoh@naver.com

Byung Seong Bae

Professor in Department of AI Smart Factory Convergence Engineering, Hoseo University. His research interest is FOM (smart-Factory Operation Management) in Display and Semiconductor Field.

E-mail: bsbae3@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