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

Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 32, No. 3, pp.169-176

ISSN: 2508-5107 (Online)

Print publication date 15 Jun 2023

Received 08 Apr 2023 Revised 15 May 2023 Accepted 18 May 2023

DOI: https://doi.org/10.7735/ksmte.2023.32.3.169

FOM-Tool Monitoring 활용 절삭공구 최적 교체 주기 설정으로 손실비용 저감

장재훈^a ; 장선준^b ; 김수영^a^{, *}

Reducing the Loss Cost by Setting the Optimal Replacement Cycle for Cutting Tools using FOM-Tool Monitoring

Jae Hoon Jang^a ; Seon Jun Jang^b ; Su Young Kim^a^{, *}

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

Correspondence to: ^*Tel.: +82-70-8600-5336 E-mail address: df2030@hoseo.edu (Su Young Kim).

Abstract

In precision machining, the condition of cutting tools deteriorates the product quality, defect rate, and productivity. Most small- and medium-size enterprises set the cutting tool replacement cycle through experience; however, this significantly differs from the actual number of tool replacements. To solve this problem, in this study, based on tool replacement data, FOM-Tool Monitoring, a smart factory operation management system, was used to perform 4M multi-dimensional analysis to analyze Min-Max and deviation in the number of times of tool use. The tool load waveform trend was also analyzed to verify the suitability of replacement, optimal replacement cycle, and conduct verification.

Keywords:

FOM(smart-factory operation management), Data file set, QPR(quick plan result), Tool monitoring, Daq(data acqusition)

Acknowledgments

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

References

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Jae Hoon Jang

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: fomsre@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