Advertisement

Sustainable Interlinked Manufacturing Processes through Real-Time Quality Prediction

  • Daniel Lieber
  • Benedikt Konrad
  • Jochen Deuse
  • Marco Stolpe
  • Katharina Morik

Abstract

Based on a rolling mill case study, this paper discusses how data mining techniques and intelligent machine-to-machine telematics could be used to predict internal quality issues of intermediate products in manufacturing processes. The huge amount of data recorded during processing and the distributed but sequential nature of the manufacturing lead to challenging questions for data mining applications and advanced process control approaches in industries like steel production. Moreover, the discovery for hidden information, knowledge and dependencies in the process data contribute significantly to support avoiding waste of resources and achieving the objectives of zero-defect-production, sustainable and energy-efficient manufacturing processes.

Keywords

Energy and resource efficiency through elimination of waste Data mining on sensor data Real-time quality prediction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Leitbild Nachhaltigkeit Stahl – Indikatoren für eine nachhaltige Entwicklung (Overall Concept Sustainablity Steel – Indicators for Sustainable Development). Stahlinstitut VDEh und Wirtschaftsvereinigung Stahl, http://www.stahl-online.de (November 02, 2011)
  2. 2.
    Wanyama, W., Ertas, A., Zhang, H.-C., Ekwaro-Osire, S.: Life-cycle engineering: issues, tools and research. International Journal of Computer Integrated Manufacturing 16(4-5), 307–316 (2003)CrossRefGoogle Scholar
  3. 3.
    Morik, K., Deuse, J., Faber, V., Bohnen, F.: Data Mining in Sensordaten verketteter Prozesse (Data Mining in Sensor Data of Interlinked Processes). In: ZWF, pp. 106–110. Carl Hanser Verlag, München (2010)Google Scholar
  4. 4.
    Haapamäki, J., Tamminen, S., Röning, J.: Data Mining Methods in Hot Steel Rolling for Scale Defect Prediction. In: International Conference on Artificial Intelligence and Applications (AIA 2005), Innsbruck, Austria, pp. 90–94 (2005)Google Scholar
  5. 5.
    Cooper, J., Vigon, B.: Life Cycle Engineering Guidelines (PDF). National Risk Management Research Laboratory, U.S. Environmental Protection Agency (2001), http://www.epa.gov (visited October 25, 2011)
  6. 6.
    Stolpe, M., Morik, K., Konrad, B., Lieber, D., Deuse, J.: Challenges for Data Mining on Sensor Data of Interlinked Processes. In: Next Generation Data Mining Summit: Ubiquitous Knowledge Discovery for Energy Management in Smart Grids and Intelligent Machine-to-Machine (M2M) Telematics, Athens, Greece (2011), http://www.kd2u.org/NGDM11
  7. 7.
    Kusiak, A., Smith, M.: Data mining in design of products and production systems. Annual Reviews in Control 31, 147–156 (2007)CrossRefGoogle Scholar
  8. 8.
    Wang, K.: Applying data mining to manufacturing: the nature and implications. Journal of Intelligent Manufacturing 18(4), 487–495 (2007)CrossRefGoogle Scholar
  9. 9.
    Harding, J.A., Shahbaz, M., Srinivas, S., Kusiak, A.: Data Mining in manufacturing: A Review. Journal of Manufacturing Sc. and Eng. 128, 969–976 (2006)CrossRefGoogle Scholar
  10. 10.
    Windt, K., Knollmann, M., Meyer, M.: Anwendung von Data Mining Methoden zur Wissensgenerierung in der Logistik (Application of Data Mining Methods for Knowledge Discovery in Logistics). In: Wissensarbeit – zwischen strengen Prozessen und kreativem Spielraum, Schriftenreihe der Hochschulgruppe für Arbeits- und Betriebsorganisation (HAB), pp. 223–249 (2011)Google Scholar
  11. 11.
    Levinson, W., Rerick, R.: Lean Enterprise: A Synergistic Approach to Minimizing Waste. American Society for Quality, Milwaukee (2002)Google Scholar
  12. 12.
    Isermann, R.: Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer (2006)Google Scholar
  13. 13.
    Kotsiantis, S., Kanellopoulos, D., Pintelas, P.: Data Preprocessing for Supervised Learning. International Journal of Computer Science 1(1), 111–117 (2006)Google Scholar
  14. 14.
    Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: Rapid Prototyping for Complex Data Mining Tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), Philadelphia, USA, pp. 935–940 (2006)Google Scholar
  15. 15.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer (2001)Google Scholar
  16. 16.
    Schölkopf, B., Platt, J.C., Shawe-Taylor, J.C., Smola, A.J., Williamson, R.C.: Estimating the Support of a High-Dimensional Distribution. Neural Comput. 13, 1443–1471 (2001)zbMATHCrossRefGoogle Scholar
  17. 17.
    Stolpe, M., Morik, K.: Learning from label proportions by optimizing cluster model selection. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 349–364. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. Symp. Math. Stat. & Prob., 281–297 (1967)Google Scholar
  19. 19.
    Alvarez, E.: Advanced Process Control to Meet the Needs of the Metallurgical Industry. World of Metallurgy – ERZMETALL 58(3), 123–128 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Daniel Lieber
    • 1
  • Benedikt Konrad
    • 1
  • Jochen Deuse
    • 1
  • Marco Stolpe
    • 2
  • Katharina Morik
    • 2
  1. 1.Industrial EngineeringTU Dortmund UniversityDortmundGermany
  2. 2.Artificial IntelligenceTU Dortmund UniversityDortmundGermany

Personalised recommendations