Advertisement

A big data analytics platform for smart factories in small and medium-sized manufacturing enterprises: An empirical case study of a die casting factory

  • Ju Yeon LeeEmail author
  • Joo Seong Yoon
  • Bo-Hyun Kim
Regular Paper

Abstract

This paper proposes an architecture and system modules for a big data analytics platform to implement smart factories in small and medium-sized enterprises. The big data analytics platform enables small and medium-sized enterprises 1) to achieve the integrated system environment between the legacy system and the platform; 2) to address quality issues by applying analytical models to their factories; and 3) to reduce their financial burdens of infrastructure and experts for the platform through cloud computing. In terms of evaluation, the proposed platform was applied to the factory of a die casting company in South Korea. Using the big data analytics platform that was developed, this paper also introduced the application scenario to identify defects in the die casting process. From this empirical research, we have clarified the difficulties and challenges in applying big data analytics to small and medium-sized manufacturing enterprises. For future works, this paper suggests a manufacturing data analytics library to provide consolidated information, including a data-mining model, its datasets, and preprocessing methods for specific manufacturing problems.

Keywords

Big data analytics platform Smart factory Small and medium-sized manufacturing enterprises Die casting process Defective casting 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kagermann, H., Helbig, J., Hellinger, A., and Wahlster, W., “Recommendations for Implementing the Strategic Initiative Industrie 4.0: Securing the Future of German Manufacturing Industry; Final Report of the Industrie 4.0 Working Group,” Forschungsunion, 2013.Google Scholar
  2. 2.
    Blanchet, M., Rinn, T., Von Thaden, G., and De Thieulloy, G., “Industry 4.0: The New Industrial Revolution-How Europe will Succeed, Roland Berger Strategy Consultants GmbH,” München, 2014.Google Scholar
  3. 3.
    Smart Manufacturing Leadership Coalition, “Implementing 21st Century Smart Manufacturing,” https://smartmanufacturingcoalition. org/sites/default/files/implementing_21st_century_smart_manufacturing _report_2011_0.pdf (Accessed 25 AUG 2017)Google Scholar
  4. 4.
    Cabinet of Japan, “Japan Revitalization Strategy: Japan’s Challenge for the Future,” http://www.kantei.go.jp/jp/singi/keizaisaisei/pdf/honbunEN.pdf (Accessed 25 AUG 2017)Google Scholar
  5. 5.
    Kang, H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., et al., “Smart Manufacturing: Past Research, Present Findings, and Future Directions,” Int. J. Precis. Eng. Manuf.-Green Tech., Vol. 3, No. 1, pp. 111–128, 2016.CrossRefGoogle Scholar
  6. 6.
    Ministry of Trade, Industry and Energy, “「Manufacturing Innovation 3.0」Initiative? Action Plans,” http://www.motie.go.kr/motie/ne/presse/press2/bbs/bbsView.do?bbs_seq_n=157086&bbs_cd_n=81 (Accessed 25 AUG 2017)Google Scholar
  7. 7.
    Wang, S., Wan, J., Li, D., and Zhang, C., “Implementing Smart Factory of Industrie 4.0: An Outlook,” International Journal of Distributed Sensor Networks, Vol. 12, No. 1, Paper No. 3159805, 2016.Google Scholar
  8. 8.
    De Mauro, A., Greco, M., and Grimaldi, M., “What is Big Data? A Consensual Definition and a Review of Key Research Topics,” Proc. of AIP Conferenc, pp. 97–104, 2015.Google Scholar
  9. 9.
    Dietrich, D., Heller, B., and Yang, B., “Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data,” Wiley & Sons, Inc., 2015.Google Scholar
  10. 10.
    Elgendy, N. and Elragal, A., “Big Data Analytics: A Literature Review Paper,” in: Industrial Conference on Data Mining, Perner, P., (Ed.), Springer, pp. 214–227, 2014.Google Scholar
  11. 11.
    Lee, J., Kao, H.-A., and Yang, S., “Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment,” Procedia CIRP, Vol. 16, pp. 3–8, 2014.CrossRefGoogle Scholar
  12. 12.
    McAfee, A. and Brynjolfsson, E., “Big Data: The Management Revolution,” Harvard Business Review, Vol. 90, No. 10, pp. 60–68, 2012.Google Scholar
  13. 13.
    Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., and Welton, C., “Mad Skills: New Analysis Practices for Big Data,” Proceedings of the VLDB Endowment, Vol. 2, No. 2, pp. 1481–1492, 2009.CrossRefGoogle Scholar
  14. 14.
    Al-Noukari, M. and Al-Hussan, W., “Using Data Mining Techniques for Predicting Future Car Market Demand; DCX Case Study,” Proc. of 3rd International Conference on Information and Communication Technologies: From Theory to Applications, pp. 1–5, 2008.Google Scholar
  15. 15.
    Chon, S. H., Slaney, M., and Berger, J., “Predicting Success From Music Sales Data: A Statistical and Adaptive Approach,” Proc. of the 1st ACM Workshop on Audio and Music Computing Multimedia, pp. 83–88, 2006.CrossRefGoogle Scholar
  16. 16.
    Provost, F. and Fawcett, T., “Data Science and Its Relationship to Big Data and Data-Driven Decision Making,” Big Data, Vol. 1, No. 1, pp. 51–59, 2013.CrossRefGoogle Scholar
  17. 17.
    Oh, J., Lee, J. Y., Yoon, J. S., and Kim, B. H., “Construction Strategy of the Smart Factory for Small and Medium-Sized Manufacturing Enterprises,” Proc. of the 25th International Conference on Flexible Automation and Intelligent Manufacturing, Vol. 2, pp. 426–433, 2015.Google Scholar
  18. 18.
    Schutt, R. and O'Neil, C., “Doing Data Science: Straight Talk from the Frontline,” O'Reilly Media, Inc., 2013.Google Scholar
  19. 19.
    Judd, C. M., McClelland, G. H., and Ryan, C. S., “Data Analysis: A Model Comparison Approach,” Routledge, 2011.Google Scholar
  20. 20.
    Karunakar, D. B. and Datta, G., “Prevention of Defects in Castings Using Back Propagation Neural Networks,” The International Journal of Advanced Manufacturing Technology, Vol. 39, Nos. 11-12, pp. 1111–1124, 2008.CrossRefGoogle Scholar
  21. 21.
    Zheng, J., Wang, Q., Zhao, P., and Wu, C., “Optimization of High-Pressure Die-Casting Process Parameters Using Artificial Neural Network,” The International Journal of Advanced Manufacturing Technology, Vol. 44, No. 7, pp. 667–674, 2009.CrossRefGoogle Scholar
  22. 22.
    Chien, C.-F., Hsu, C.-Y., and Chen, P.-N., “Semiconductor Fault Detection and Classification for Yield Enhancement and Manufacturing Intelligence,” Flexible Services and Manufacturing Journal, Vol. 25, No. 3, pp. 367–388, 2013.CrossRefGoogle Scholar
  23. 23.
    Demetgul, M., “Fault Diagnosis on Production Systems with Support Vector Machine and Decision Trees Algorithms,” The International Journal of Advanced Manufacturing Technology, Vol. 67, Nos. 9-12, pp. 2183–2194, 2013.CrossRefGoogle Scholar
  24. 24.
    He, S.-G., He, Z., and Wang, G. A., “Online Monitoring and Fault Identification of Mean Shifts in Bivariate Processes Using Decision Tree Learning Techniques,” Journal of Intelligent Manufacturing, Vol. 24, No. 1, pp. 25–34, 2013.CrossRefGoogle Scholar
  25. 25.
    Lee, J. Y., Ph, J., Kim B.-H., and Yoon, J.-S., “Identification of Defects in a Die Casting Process Based on Data Analytics,” Proc. of the 25th International Conference on Flexible Automation and Intelligent Manufacturing, Vol. 1, pp. 174–180, 2015.Google Scholar

Copyright information

© Korean Society for Precision Engineering and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.IT Converged Process R&D GroupKorea Institute of Industrial TechnologyGyeonggi-doSouth Korea
  2. 2.Smart Manufacturing Technology GroupKorea Institute of Industrial TechnologyChungcheongnam-doSouth Korea

Personalised recommendations