A Hierarchical Evaluation Scheme for Industrial Process Chains: Aluminum Die Casting

  • Tim Heinemann
  • Wataru Machida
  • Sebastian Thiede
  • Christoph Herrmann
  • Sami Kara


Industrial process chains can consist of differing sub-process-chains with varying degrees of complexity and dynamic behavior. This fact makes it difficult to compare measures for enhancing the energy and resource efficiency of such systems. As a prerequisite a common basis for assessing the relevance of improvement measures that take effect on different system levels needs to be established. Against this background the paper proposes a hierarchical evaluation scheme for the aluminum die casting process chain that addresses the aforementioned process attributes in terms of data accuracy, dynamic behavior of system elements on different levels or the system as a whole.


Process Chain Evaluation Simulation Aluminum Die Casting 


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  1. 1.
    Worrell, E., Price, L., Neelis, M., Galitsky, C., Nan, Z.: World Best Practice Energy Intensity Values for Selected Industrial Sectors. Ernest Orlando Lawrence Berkeley National Laboratory (2008)Google Scholar
  2. 2.
    Trimet Aluminium AG: Annual Report 2007, Essen, Germany (2007)Google Scholar
  3. 3.
    Krinke, S.: Implementing Life Cycle Engineering efficiently into Automotive Industry Processes. In: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering (LCE 2011), Braunschweig, Germany, pp. 11–16 (2011)Google Scholar
  4. 4.
  5. 5.
    Kumar, S.A., Suresh, N.: Production and Operations Management. New Age International Publishers (2006)Google Scholar
  6. 6.
    Herrmann, C., Kara, S., Thiede, S., Luger, T.: Energy Efficiency in Manufacturing – Perspectives from Australia and Europe. In: Proceedings of the 17th CIRP International Conference on Life Cycle Engineering (LCE 2010), Hefei, China, pp. 23–28 (2010)Google Scholar
  7. 7.
    ISO 9000:2000, 3.4.1Google Scholar
  8. 8.
    Kuhn, A.: Handbuch Logistik. VDI-Buch, Springer (2002)Google Scholar
  9. 9.
    Herrmann, C., Thiede, S., Kara, S., Hesselbach, J.: Energy oriented simulation of manufacturing systems – Concept and application. Annals of the CIRP 60(1), 45–48 (2011)CrossRefGoogle Scholar
  10. 10.
    Kara, S., Manmek, S., Herrmann, C.: Global Manufacturing & the Embodied Energy of Products. CIRP Annals – Manufacturing Technology 59, 29–32 (2010)CrossRefGoogle Scholar
  11. 11.
    Eisele, C., Schrems, S., Abele, E.: Energy-Efficient Machine Tools through Simulation in the Design Process. In: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering (LCE 2011), Braunschweig, Germany, pp. 258–262 (2011)Google Scholar
  12. 12.
    Dietmair, A., Verl, A.: A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing. International Journal of Sustainable Engineering 2(2), 123–133 (2009)CrossRefGoogle Scholar
  13. 13.
    Li, W., Kara, S.: Unit Process Energy Consumption Models for Manufacturing Processes. Annals of the CIRP 60(1), 37–40 (2011)CrossRefGoogle Scholar
  14. 14.
    Solding, P., Petku, D., Mardan, N.: Using simulation for more sustainable production systems – methodologies and case studies. International Journal of Sustainable Engineering 2(2), 111–122 (2009)CrossRefGoogle Scholar
  15. 15.
    Heilala, J., Vatanen, S., Tonteri, H., Montonen, J., Lind, S., Johansson, B., Stahre, J.: Simulation-based sustainable manufacturing system design. In: Mason, S.J. (ed.) Winter Simuation Conference (WSC 2008), pp. 1922–1930. IEEE, Miami (2008)CrossRefGoogle Scholar
  16. 16.
    Junge, M.: Simulationsgestützte Entwicklung und Optimierung einer energieeffizienten Produktionssteuerung, Dissertation. Universität Kassel, Produktion & Energie, Kassel, vol. 1 (2007)Google Scholar
  17. 17.
    Wohlgemuth, V., Page, B., Kreutzer, W.: Combining discrete event simulation and material flow analysis in a component-based approach to industrial environmental protection. Environmental Modelling & Software 21(11), 1607–1617 (2006)CrossRefGoogle Scholar
  18. 18.
    Löfgren, B., Tillman, A.-M.: Relating manufacturing system configuration to life-cycle environmental performance: discrete-event simulation supplemented with LCA. Journal of Cleaner Production 19, 2015–2024 (2011)CrossRefGoogle Scholar
  19. 19.
    VAR – Verband der Aluminiumrecycling-Industrie e.V., Key Figures – Production of Aluminium in Germany,
  20. 20.
    Herrmann, C., Heinemann, T., Thiede, S.: Identifying levers for Enhancing Energy and Resource Efficiency in Industrial Process Chains using the example of Aluminium Die Casting. In: Proceedings of the 1st International Conference on Automotive Materials and Manufacturing (AM&M 2010), Pune, India (2010)Google Scholar
  21. 21.
    Anders, U., Pries, H., Dilger, K.: Ökologisch und ökonomisch optimierter Trennstoffeinsatz beim Aluminium-Druckguss, research project, BMBF, 01RW0055, Braunschweig, Germany (2001-2003)Google Scholar
  22. 22.
    Hartmann, G., Seefeldt, R., Franke, J.: Simulation tools – NG: Practical application of next generation numerical optimization to HPDC casting processes,
  23. 23.
    Abele, E., Sielaff, T., Schiffler, A., Rothenbücher, S.: Analyzing Energy Consumption of Machine Tool Spindle Units and Identification of Potential for Improvements of Efficiency. In: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering (LCE 2011), Braunschweig, Germany, pp. 280–285 (2011)Google Scholar
  24. 24.
    Li, W., Zein, A., Kara, S., Herrmann, C.: An Investigation into Fixed Energy Consumption of Machine Tools. In: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering (LCE 2011), Braunschweig, Germany, pp. 268–273 (2011)Google Scholar
  25. 25.
    Machida, W., Heinemann, T., Thiede, S., Herrmann, C., Kara, S.: Environmental Management Accounting and Decisions based on Matrix Algebra of Material Flow Cost Accounting, Life Cycle Inventory Analysis and Activity Based Costing (Working Paper, submitted to the International Journal of Life Cycle Assessment) (2011)Google Scholar
  26. 26.
    Magma Gießereitechnologie GmbH, presentation at 10. Karlsruher Arbeitsgespräche Produktionsforschung 2010, Karlsruhe, Germany (2010)Google Scholar
  27. 27.
    Herrmann, C., Heinemann, T., Thiede, S.: Synergies from Process and Energy Oriented Process Chain Simulation – A Case Study from the Aluminium Die Casting Industry. In: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering (LCE 2011), Braunschweig, Germany, pp. 317–322 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tim Heinemann
    • 1
    • 2
  • Wataru Machida
    • 1
    • 2
    • 3
  • Sebastian Thiede
    • 1
    • 2
  • Christoph Herrmann
    • 1
    • 2
  • Sami Kara
    • 1
    • 3
  1. 1.Joint German-Australian Research Group “Sustainable Manufacturing and Life Cycle Management”Technische Universität BraunschweigBraunschweigGermany
  2. 2.Product- and Life-Cycle-Management Research Group, Institute of Machine Tools and Production TechnologyTechnische Universität BraunschweigBraunschweigGermany
  3. 3.Life Cycle Engineering and Management Research Group, School of Mechanical & Manufacturing EngineeringThe University of New South WalesSydneyAustralia

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