Abstract
To gain a better and deeper understanding of cause and effect dependencies in complex production processes it is necessary to represent these processes for analysis as good and complete as possible. Virtual production is a main contribution to reach this objective. To use the Virtual Production effectively in this context, a base that allows a holistic, integrated view of information that is provided by IT tools along the production process has to be created. The goal of such an analysis is the possibility to identify optimization potentials in order to increase product quality and production efficiency. The presented work will focus on a simulation based planning phase of a production process as core part of the Virtual Production. An integrative approach which represents the integration, analysis and visualization of data generated along such a simulated production process is introduced. This introduced system is called Virtual Production Intelligence and in addition to the integration possibilities it provides a context-sensitive information analysis to gain more detailed knowledge of production processes.
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References
VDI Richtlinie 4499, Blatt 1, Digital factory. Tech. rep., 2008
VDI Richtlinie 4499, Blatt 2, Digital factory. Tech. rep., 2011
D. Schilberg, T. Meisen, R. Reinhard, Virtual production – the connection of the modules through the virtual production intelligence. In: Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science 2013, WCECS 2013, 23-25 October, 2013, San Francisco, USA. pp. 1047–1052
R. Lauber, P. Göhner, Prozessautomatisierung 1, 3rd edn. Springer, Berlin, 1999
H. Kagermann, W. Wahlster, J. Helbig, Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0 – Ab-schlussbericht des Arbeitskreises Industrie 4.0. Forschungsunion im Stifterverband für die Deutsche Wissenschaft, Berlin, 2012
DIN EN ISO 10303
M. Nagl, B. Westfechtel, Modelle, Werkzeuge und Infrastrukturen zur Unterstützung von Entwicklungsprozessen, 1st edn. Symposium (Forschungsbericht (DFG)). Wiley-VCH, 2003
C. Horstmann, Integration und Flexibilitat der Organisation Durch Informationstechnologie, 1st edn. Gabler Verlag, 2011
D. Schilberg, Architektur eines Datenintegrators zur durchgängigen Kopplung von verteilten numerischen Simulationen. VDI-Verlag, Aachen, 2010
T. Meisen, P. Meisen, D. Schilberg, S. Jeschke, Application integration of simulation tools considering domain specific knowledge. In: Proceedings of the 13th International Conference on Enterprise Information Systems. 2011
R. Reinhard, T. Meisen, T. Beer, D. Schilberg, S. Jeschke, A framework enabling data integration for virtual production. In: Enabling Manufacturing Competitiveness and Economic Sustainability – Proceedings of the 4th International Conference on Changeable, Agile, Reconfigurable and Virtual production (CARV2011), Montreal, Canada, 2-5 October 2011, ed. by A.E. v. Hoda. Berlin Heidelberg, 2012, pp. 275–280
B. Byrne, J. Kling, D. McCarty, G. Sauter, P. Worcester, The Value of Applying the Canonical Modeling Pattern in SOA. IBM (The information perspective of SOA design, 4). 2008
M. West, Developing High Quality Data Models, 1st edn. Morgan Kaufmann, Burlington, MA, 2011
W. Yeoh, A. Koronios, Critical success factors for business intelligence systems. Journal of computer information systems 50 (3), 2010, p. 23
ISO/IEC 2382-01
M. Daconta, L. Obrst, K. Smith, The Semantic Web: The Future of XML, Web Services, and Knowledge Management. 2003
D. Schilberg, A. Gramatke, K. Henning, Semantic interconnection of distributed numerical simulations via soa. In: Proceedings World Congress on Engineering and Computer Science 2008, ed. by I.A. of Engineers. Newswood Limited, Hong Kong, 2008, pp. 894–897
D. Schilberg, T. Meisen, R. Reinhard, S. Jeschke, Simulation and interoperability in the planning phase of production processes. In: ASME 2011 International Mechanical Engineering Congress & Exposition, ed. by ASME. Denver, 2011
A. Saltelli, S. Tarantola, F. Campolongo, M. Ratto, Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley & Sons, Ltd., 2004
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Schilberg, D., Meisen, T., Reinhard, R. (2016). Virtual Production Intelligence – Process Analysis in the Production Planning Phase. In: Frerich, S., et al. Engineering Education 4.0. Springer, Cham. https://doi.org/10.1007/978-3-319-46916-4_11
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DOI: https://doi.org/10.1007/978-3-319-46916-4_11
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