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The Contribution of Virtual Production Intelligence to Laser Cutting Planning Processes

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Enabling Manufacturing Competitiveness and Economic Sustainability

Abstract

In order to facilitate the improvement in product quality and production efficiency, many companies use simulation applications. In turn, they face the challenge of making these applications interoperable. Once the interoperability is established, the challenges of understanding and improving the processes arise. They can be overcome by modeling and analyzing the processes in question. This paper presents a use case scenario from laser cutting. A new concept is introduced addressing the challenges aforementioned. It conforms to the principles of the integration and examination of data and combines virtual production with the goal of gaining knowledge through the analysis of simulated processes.

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References

  1. Schuh, G., Aghassi, S., Orilski, S., Schubert, J., Bambach, M., Freudenberg, R., Hinke, C., Schiffer, M.: Technology roadmapping for the production in high-wage countries. Prod. Eng. Res. Devel. (Production Engineering) 5, 463–473 (2011)

    Google Scholar 

  2. Ilschner, B., Singer, R.: Werkstoffwissenschaften und Fertigungstechnik: Eigenschaften, Vorgänge, Technologien. Springer (2010)

    Google Scholar 

  3. Schmitz, G.J., Prahl, U.: Integrative computational materials engineering. online resource, p. 1. Wiley-VCH, Weinheim (2012) (online resource)

    Google Scholar 

  4. Verein Deutscher Ingenieure, Digital factory - Fundamentals, Berlin (2008)

    Google Scholar 

  5. Verein Deutscher Ingenieure, Digital Factory - Digital Factory Operations, Berlin (2011)

    Google Scholar 

  6. Nagl, M., Westfechtel, B.: Modelle, Werkzeuge und Infrastrukturen zur Untersttzung von Entwicklungsprozessen: Symposium. John Wiley & Sons (2003)

    Google Scholar 

  7. Horstmann, C.: Integration und Flexibilität der Organisation durch Informationstechnologie. Gabler Verlag (2011)

    Google Scholar 

  8. Hoberman, S.: Canonical Data Model (2008), http://www.information-management.com/issues/2007_50/10001733-1.html (accessed February 25, 2013)

  9. Schilberg, D.: Architektur eines Datenintegrators zur durchgängigen Kopplung von verteilten numerischen Simulationen. VDI-Verlag, Aachen (2010)

    Google Scholar 

  10. Meisen, T., Meisen, P., Schilberg, D., Jeschke, S.: Application Integration of Simulation Tools Considering Domain Specific Knowledge. In: Automation, Communication and Cybernetics in Science and Engineering 2011/2012, pp. 1067–1089. Springer (2013)

    Google Scholar 

  11. Byrne, B., Kling, J., McCarty, D., Sauter, G., Worcester, P.: The value of applying the canonical modeling pattern. In: SOA, vol. 4 (2008)

    Google Scholar 

  12. West, M.: Developing High Quality Data Models. Elsevier Science (2011)

    Google Scholar 

  13. Bracht, U., Geckler, D., Wenzel, S.: Digitale Fabrik, p. 424. Springer, Heidelberg (2011)

    Book  Google Scholar 

  14. Luhn, H.: A Business Intelligence System. IBM Journal, 314–319 (1958)

    Google Scholar 

  15. Reinhard, R., Büscher, C., Meisen, T., Schilberg, D., Jeschke, S.: Virtual Production Intelligence – A Contribution to the Digital Factory. In: Su, C.-Y., Rakheja, S., Liu, H. (eds.) ICIRA 2012, Part I. LNCS, vol. 7506, pp. 706–715. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  16. Poprawe, R., Schulz, W., Schmitt, R.: Hydrodynamics of material removal by melt expulsion: Perspectives of laser cutting and drilling. Physics Procedia 5, 1–18 (2010)

    Article  Google Scholar 

  17. Jurecka, F.: Robust design optimization based on metamodeling techniques. Shaker Verlag (2007)

    Google Scholar 

  18. Schulz, W.: Die Dynamik des thermischen Abtrags mit Grenzschichtcharakter. Shaker (2003)

    Google Scholar 

  19. Schulz, W., Niessen, M., Eppelt, U., Kowalick, K.: Simulation of Laser Cutting. In: The Theory of Laser Materials Processing, pp. 21–69. Springer, Dordrecht (2009)

    Chapter  Google Scholar 

  20. Orr, M.: Introduction to radial basis function networks (2013), http://dns2.icar.cnr.it/manco/Teaching/2005/datamining/articoli/RBFNetworks.pdf (accessed March 6, 2013)

  21. Rippa, S.: An algorithm for selecting a good value for the parameter c in radial basis function interpolation. Advances in Computational Mathematics 11, 193–210 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  22. Jones, D., Schonlau, M., Welch, W.: Efficient Global Optimization of Expensive Black-Box Functions 13, 455–492 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  23. Martin, J., Simpson, T.: Use of Kriging Models to Approximate Deterministic Computer Models. Aiaa Journal 43(4), 853–863 (2005)

    Article  Google Scholar 

  24. Haykin, S.: Neural Networks and Learning Machines, 3 Hrsg. Pearson Education (2009)

    Google Scholar 

  25. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Boston (1989)

    MATH  Google Scholar 

  26. Yoel, T., Chi-Keong, G.: Computational Intelligence in Optimization, vol. 7. Springer, Berlin (2010)

    Google Scholar 

  27. Gerber, S., Potter, K.: Data Analysis with the Morse-Smale Complex: The msr Package for R. Journal of Statistical Software 50(2), 1–22 (2012)

    Google Scholar 

  28. The R Foundation for Statistical Computing, The R Project for Statistical Computing (2013), http://www.r-project.org/ (accessed February 27, 2013)

  29. Fetter, I., Melnikov, A.: The WebSocket Protocol (2011), http://tools.ietf.org/html/rfc6455 (accessed February 26, 2013)

  30. RStudio, I.: Easy web applications in R (2013), http://www.rstudio.com/shiny/ (accessed March 6, 2013)

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Reinhard, R. et al. (2014). The Contribution of Virtual Production Intelligence to Laser Cutting Planning Processes. In: Zaeh, M. (eds) Enabling Manufacturing Competitiveness and Economic Sustainability. Springer, Cham. https://doi.org/10.1007/978-3-319-02054-9_20

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  • DOI: https://doi.org/10.1007/978-3-319-02054-9_20

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02053-2

  • Online ISBN: 978-3-319-02054-9

  • eBook Packages: EngineeringEngineering (R0)

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