[ 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

오상석^a ; 장선준^b ; 김수영^a^{, *}

Prediction of Productivity Improvement Applying Simulation and FOM based on Final Machining Process Data of Large Chamber

Sang Suk Oh^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.: +82-41-540-9960 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).

References

Ko, D. B., Park, J. M., 2018, A Study on the Visualization of Facility Data Using Manufacturing Data Collection Standard, The Journal of The Institute of Internet, Broadcasting and Communication, 18:3 159-166. [https://doi.org/10.7236/JIIBC.2018.18.3.159]
Bae, S. M., 2017, Intelligent Plant: Smart Factory, Review of Korea Contents Association, 15:2 21-24.
Liang, S., Rajora, M., Liu, X., Yue, C., Zou, P., Wang, L., 2018, Intelligent Manufacturing System: A Review, International Journal of Mechanical Engineering and Robotics Research, 7:3 324-330. [https://doi.org/10.18178/ijmerr.7.3.324-330]
Park, J. K., Chang, T. W., 2018, Review of Domestic Research on Smart Manufacturing Technologies, The Journal of Society for e-Business Studies, 23:2 123-133. [https://doi.org/10.7838/jsebs.2018.23.2.123]
Kim, J. S., Cho, W. S., 2015, Data Analysis of 4M Data in Small and Medium Enterprises, Journal of the Korean Data and Information Science Society, 26:5 1117-1128. [https://doi.org/10.7465/jkdi.2015.26.5.1117]
Noh, K. -S., Park, S., 2014, An Exploratory Study on Application Plan of Big Data to Manufacturing Execution System, Journal of Digital Convergence, 12:1 305-311. [https://doi.org/10.14400/JDPM.2014.12.1.305]
Hong, Y. H., Kim, C. R., 2014, Recent Developments of Constructing Adjacency Matrix in Network Analysis, Journal of the Korean Data & Information Science Society, 25:5 1107-1116. [https://doi.org/10.7465/jkdi.2014.25.5.1107]
Hwang, S. M., 2003, The Comparative Study of a Manual Assembly Line and Alternative Plans for the Productivity Improvement, Productivity Review, 17:3 55-72.
Cho, K. K, Moon, I. K., Yun, W. Y., Kim, Y. K., 1999, A Simulation Study to Analyze Production and Material Flow of a Microwave Oven Assembly Line, Journal of Digital Convergence, 12:1 121-131.
Oh, P. B., Rim, S. C., Han, H. S., 2000, Improved Design of Engine Manufacturing Line Using Simulation, Journal of the Korea Society for Simulation, 9:1 1-9.
Charies, H., Robert, E. B., Thomas, J. G., Mott, R. A., 1966, System Improvement Using Simulation, Promodel Corporation, Orem Utah.
Kim, S. Y., 2018, A Case Study of the Introduction of Smart Factory Operation Management(FOM) in the Fourth Industrial Revolution Era, Korean Association of Computers and Accounting, 16:1 43-62. [https://doi.org/10.32956/kaoca.2018.16.1.43]
Oh, S. S., Yang, H. S., Bae, B. S., Kim, S. Y., 2021, Application of FOM Methodology for 4M Optimization Based on the Data of Manufacturing Process of Mechanical Parts, J. Korean Soc. Manuf. Technol. Eng., 30:6 456-464. [https://doi.org/10.7735/ksmte.2021.30.6.456]
Kim, J. H., Kim, S. Y., 2021, Productivity Analysis Method based on Manufacturing Big-data using the FOM System in the FOMs Package, J. Korean Soc. Manuf. Technol. Eng., 30:4 259-268. [https://doi.org/10.7735/ksmte.2021.30.4.259]
Wang, L., Wang, G. H., 2016, Big Data in Cyber-Physical Systems, Digital Manufacturing and Industry 4.0, International Journal of Engineering and Manufacturing(IJEM), 6:4 1-8. [https://doi.org/10.5815/ijem.2016.04.01]
Song, Y. J., Woo, J. H., Lee, D. K., Shin, J. G., 2008, A Simulation Study for Evaluation of Alternative Plans and Making the Upper-limit for Improvement in Productivity of Flow-shop with Considering a Work-wait Time, The Korea Society for Simulation, 17:2 63-74.
Jeong, B. H., La, S. Y., Park, B. E., Zhang, Y. S., 2011, A Case Study for Reducing Exchange Time of Die in Punch Press Process with Various Die Height, Journal of the Society of Korea Industrial and Systems Engineering, 34:2 103-111.
Kim, J. D., Song, Y. W., Cho, W. S., 2016, The Usage Needs and Adoption Intention of Manufacturing Big Data Technology in Small and Medium-sized Manufacturing Companies, Korean Corporation Management Review, 23:5 47-68.

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