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The virtual production simulation platform: from collaborative distributed simulation to integrated visual analysis

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Abstract

Computational simulations are used for the optimization of production processes in order to significantly reduce the need for costly experimental optimization approaches. Yet individual simulations can rarely describe more than a single production step. A set of simulations has to be coupled to each other to form a contiguous representation of a process chain. Eventually, simulated results have to be analyzed by domain experts to gather insight from the preformed computations. In this paper, we propose an IT infrastructure and software tools that aim at a rather non-intrusive way of coupling resources and domain expert’s knowledge to enable the collaborative setup, execution and analysis of distributed simulation chains. We illustrate the approach in the domain of materials processing. Beyond means originating from the domain of GRID computing for resource management, a data integration component assures semantic data integrity between the simulation steps and stores simulation data in an application independent way. Thus, we can transform this data into native formats for each simulation tool, and finally into a format that allows for contiguous visualizations and an intuitive, comprehensive analysis of complete simulated process chains.

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References

  1. Foster I, Kesselman C (1999) The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, CA

    Google Scholar 

  2. Thain D, Tannenbaum T, Livny M (2005) Distributed computing in practice: the condor experience. Concurr Comput pract exper 17:323–356. doi:10.1.1.6.3035

    Article  Google Scholar 

  3. Cerfontaine P, Beer T, Kuhlen T, Bischof C (2008) Towards a flexible and distributed simulation platform. Int.Conf Comput Sci Appl 867–882. doi:10.1007/978-3-540-69839-5_65

  4. Halevy A, Rajaraman A, Ordille J (2006) Data integration: the teenage years: Proceedings of the 32nd international conference on Very large data bases (VLDB), 9–16

  5. Hohpe G, Woolf B (2003) Enterprise integration patterns: designing, building, and deploying messaging solutions. Addison, Wesley Longman

    Google Scholar 

  6. Chappell D (2004) Enterprise Service Bus: Theory in Practice. O’Reilly Media

  7. White C (2005) Data Integration: Using ETL, EAI, and EII tools to create an integrated enterprise. The Data Warehousing Institute

  8. Leser U (2007) Informationsintegration : architekturen und methoden zur integration verteilter und heterogener datenquellen. Dpunkt-Verlag, Heidelberg

    MATH  Google Scholar 

  9. Lavigne C, (2006) Advanced ETL with pentaho data integration. Breadboard BI

  10. Zimmermann A, et. al. (2006) Formalizing ontology alignment and its operations with category theory. In: Proceedings of 4th international conference on formal ontology in information systems (FOIS), 277–288

  11. Euzenat J, Shvaiko P (2007) Ontology matching. Springer, Berlin

    MATH  Google Scholar 

  12. Staab S, Studer R (2009) Handbook on ontologies. Springer, Berlin

    Book  Google Scholar 

  13. Hoffmann J, Nebel B (2001) The planning system: fast plan generation through heuristic search. J Artif Intell Res 14

  14. Schroeder W, Martin K, Lorensen B (2006) The Visualization Toolkit. Kitware, Inc., New York

  15. Henderson A (2008) The ParaView Guide. Kitware, Inc., New York

  16. Heok TK, Daman D (2004) A review on level of detail. IEEE Comput Graph Imaging and Vis 70–75. doi:10.1109/CGIV.2004.1323963

  17. Hoppe H (1999) New quadric metric for simplifying meshes with appearance attributes. IEEE Vis 59–66. doi:10.1.1.2.9544

  18. Valette S, Chassery JM (2004) Approximated centroidal voronoi diagrams for uniform polygonal mesh coarsening. Comput Graph Forum. doi:10.1111/j.1467-8659.2004.00769.x

  19. Schroeder WJ, Zarge JA, Lorensen WE (1992) Decimation of triangle meshes, SIGGRAPH 65–70. doi:10.1145/133994.134010

  20. (2010) ViSTA VR Toolkit. http://vistavrtoolkit.sourceforge.net. Accessed 01 October 2010

  21. Assenmacher I, Kuhlen T (2008) The ViSTA Virtual Reality Toolkit, SEARIS Workhop on IEEE VR 2008, Shaker, Aachen

Download references

Acknowledgments

The depicted research has been funded by the German Research Foundation DFG as part of the Cluster of Excellence “Integrative Production Technology for High-Wage Countries”.

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Correspondence to Thomas Beer.

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Beer, T., Meisen, T., Reinhard, R. et al. The virtual production simulation platform: from collaborative distributed simulation to integrated visual analysis. Prod. Eng. Res. Devel. 5, 383–391 (2011). https://doi.org/10.1007/s11740-011-0326-x

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