DOI QR코드

DOI QR Code

Function Expansion of Human-Machine Interface(HMI) for Small and Medium-sized Enterprises: Focused on Injection Molding Industries

중소기업을 위한 인간-기계 인터페이스(HMI) 기능 확장: 사출성형기업 중심으로

  • Sungmoon, Bae (School of Industrial and Systems Engineering, Gyeongsang National University) ;
  • Sua, Shin (School of Industrial and Systems Engineering, Gyeongsang National University) ;
  • Junhong, Yook (School of Industrial and Systems Engineering, Gyeongsang National University) ;
  • Injun, Hwang (KOSCA Co. Ltd.)
  • 배성문 (경상국립대학교 산업시스템공학과) ;
  • 신수아 (경상국립대학교 산업시스템공학과) ;
  • 육준홍 (경상국립대학교 산업시스템공학과) ;
  • 황인준 (코스카)
  • Received : 2022.11.28
  • Accepted : 2022.12.14
  • Published : 2022.12.31

Abstract

As the 4th industrial revolution emerges, the implementation of smart factories are essential in the manufacturing industry. However, 80% of small and medium-sized enterprises that have introduced smart factories remain at the basic level. In addition, in root industries such as injection molding, PLC and HMI software are used to implement functions that simply show operation data aggregated by facilities in real time. This has limitations for managers to make decisions related to product production other than viewing data. This study presents a method for upgrading the level of smart factories to suit the reality of small and medium-sized enterprises. By monitoring the data collected from the facility, it is possible to determine whether there is an abnormal situation by proposing an appropriate algorithm for meaningful decision-making, and an alarm sounds when the process is out of control. In this study, the function of HMI has been expanded to check the failure frequency rate, facility time operation rate, average time between failures, and average time between failures based on facility operation signals. For the injection molding industry, an HMI prototype including the extended function proposed in this study was implemented. This is expected to provide a foundation for SMEs that do not have sufficient IT capabilities to advance to the middle level of smart factories without making large investments.

Keywords

References

  1. Bae, D.S., Latest Statistical Quality Control, revision, Youngji Publishers, 2007. 
  2. Cho, S.M., Failure Prediction Method for Wafer Transfer Robot, SungKyunKwan University, 2013. 
  3. Hare, L.B., Follow the Rules, Quality Progress, 2013, Vol. 46, No. 1, pp. 56-57. 
  4. Hwang, K.Y., Kwak, Y.K., Park, J.K., Lee, S.W., Lee, J.K., Nam, S.J., and Ahn, J.H., Development of HMI system for Data Acquisition and Transmission, In Proceedings of the Korean Society of Precision Engineering Conference, 2010, pp. 469-470. 
  5. Kang, S.W., Analysis of Predictive Maintenance Model of Facility for Smart Factory, Yonsei University, 2018. 
  6. Kim, H.D., Kim, D.M., Lee, K.G., Yoon, J.W., and Youm, S., Development of Smart Factory Diagnostic Model Reflecting Manufacturing Characteristics and Customized Application of Small and Medium Enterprises, Journal of the Society of Korea Industrial and Systems Engineering, 2019, Vol. 42, No. 3, pp. 25-38.  https://doi.org/10.11627/jkise.2019.42.3.025
  7. Kim, S.M. and Ahn, J.K., A Case Study on Smart Factory Extensibility for Small and Medium Enterprises, Journal of the Society of Korea Industrial and Systems Engineering, 2021, Vol. 44, No. 2, pp. 43-57. 
  8. Kim, Y.S. and Shin, H.J., Failure and Diagnosis Information Supporting System for Maintenance Management of Logistics Equipment, Journal of the Korean Institute of Plant Engineering, 2004, pp. 27-41. 
  9. Kim, Y.U., Im, J.I., Jeong, J.S., Lee, S.H., Kim, Y.J., and Cha, S.G., Field conditions and evolution models for smart factories, Magazine of the IEIE, 2016. 
  10. Lee, M.S., Kim, K.S., and Chae, S.M., predictive maintenance based facility management system for Diecasting facility, The Korean Society of Mechanical Engineers, 2017.11, pp. 2731-2734. 
  11. Lee, Y.J., Cheo, H.C., Lee, J.W., Lee, K.S., and Kim, T.G., A Development of Auto Inspection System using HMI for Electric Control Panel, In Proceedings of the KIEE Conference, 2007, pp. 138-142. 
  12. Nam, S.H., Kang, H.W., Rye, K.Y., Lee, S.W., and Choi, H.J., Network-Based Architecture and Design of Human-Machine Interface, 2006, Proceedings of the Institution of Mechanical Engineers, pp. 1430-1433. 
  13. Nelson, LS., The Shewhart control chart- tests for special causes, Journal of Quality Technology, 1984, Vol. 16, No. 4, pp. 237-239.  https://doi.org/10.1080/00224065.1984.11978921
  14. Oh, J.H. and Kim, J.D., A Study on Strategic Utilization of Smart Factory: Effects of Building Purposes and Contents on Continuous Utilization, Asia Pacific Journal of Small Business, 2019, Vol. 41, No. 2, pp. 1-36. 
  15. Park, C., Moon, D., Do, N., and Bae, S. M., A predictive maintenance approach based on real-time internal parameter monitoring, The International Journal of Advanced Manufacturing Technology, 2016, Vol. 85, No. 1, pp. 623-632.  https://doi.org/10.1007/s00170-015-7981-6
  16. Ro, Y., Strategy Trends in Principal Countries toward the 4th Industrial Revolution, Electronics and Telecommunications Trends, 2017, Vol. 32, No. 2, pp. 1-9. 
  17. Seong, J. and Jeong, J., Design and Implementation of OCR-based Machine Monitoring System for Small and Medium-Sized Enterprise (SMEs), The Journal of the Institute of Internet, Broadcasting and Communication, 2021, Vol. 21, No. 3, pp. 73-79. https://doi.org/10.7236/JIIBC.2021.21.3.73