[ Special Issue : SW-based smart-Factory Operation Managements(FOMs) Technology ]

Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 32, No. 3, pp.177-181

ISSN: 2508-5107 (Online)

Print publication date 15 Jun 2023

Received 08 Apr 2023 Revised 10 May 2023 Accepted 23 May 2023

DOI: https://doi.org/10.7735/ksmte.2023.32.3.177

FOM을 활용한 문형 오면가공기계의 가공 리드타임 단축 연구

이남은^a ; 장선준^b ; 김수영^a^{, *}

Study on Reduction of Machining Lead Time of Double Surface Machining Machine using FOM

Nam Eun Lee^a ; Seon Jun Jang^b ; Su Young Kim^a^{, *}

aDepartment of AI Smart Factory Convergence Engineering, Hoseo University
bDivision of Mechanical and Automotive Engineering, Hoseo University

Correspondence to: ^*Tel.: +82-2-3473-5336 E-mail address: df2030@hoseo.edu (Su Young Kim).

Abstract

The digital transformation of small and medium enterprises is necessary for the improvement of quality and productivity through the collection and analysis of manufacturing data and to improve delivery compliance for competitiveness. For example, it implements a smart factory operation management solution by analyzing data to reduce long lead times in machining processes and efficiently operate machines in manufacturing large vacuum chambers. The improvement effect is improved after the loss reduction for the long processing lead time of the processing machine. Therefore, many processing companies that manufacture small quantities of small items must achieve the process optimization of custom-made products and continuously promote activities to improve productivity, cost reduction, the total lead time, and profitability through fast manufacturing-level smart factory data flexibility.

Keywords:

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

Acknowledgments

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

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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

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