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Integrating the Mechanical Domain into Seed Approach

Part of: Knowledge-based Engineering

Published online by Cambridge University Press:  26 July 2019

Fabian Dworschak ,
Patricia Kügler ,
Benjamin Schleich  and
Sandro Wartzack

Abstract

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Data-driven technologies have found their way into all areas of engineering. In product development they can accelerate the customization to individualized requirements. Therefore, they need a database that exceeds common product data management systems. The creation of this database proves to be challenging because in addition to explicit standards and regulations the product design contains implicit knowledge of product developers. Hence, this paper presents an approach for the semantic integration of the engineering design (SeED). The goal is an automated design of an ontology, which represents the product design in detail.

SeED fulfils two tasks. First, the ontology provides a machine-processable representation of the products design, which enables all kind of data-driven technologies. Among other representations, the ontology contains formal logics and semantics. Accordingly, it is a more comprehensible solution for product developers and knowledge engineers. Second, the detailed representation enables discovering of intrinsic knowledge, e.g. design patterns in product generations. Consequently, SeED is a novel approach for efficient semantic integration of the product design.

Keywords

OntologiesSemantic data processingKnowledge management
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 2587 - 2596
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

References

Albers, A., Ebel, B. and Sauter, C. (2010), “Combining process model and semantic wiki”, 11th International Design Conference, Dubrovnik, Croatia, May 17-20, Design Society, Glasgow, UK, pp. 1275–1284.Google Scholar
Baader, F. (2005), The description logic handbook: Theory, implementation, and applications, Cambridge Univ. Press, Cambridge. http://doi.org/10.1017/CBO9780511711787.Google Scholar
Feldhusen, J. and Grote, K.-H. (2013), Pahl/Beitz Konstruktionslehre, Springer, Berlin, Heidelberg. http://doi.org/10.1007/978-3-642-29569-0.Google Scholar
Grangel-Gonzalez, I., Halilaj, L., Auer, S., Lohmann, S., Lange, C. and Collarana, D. (2016), “An RDF-based approach for implementing industry 4.0 components with Administration Shells”, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, Germany, 06.-09.09.2016, IEEE, pp. 1–8. http://doi.org/10.1109/ETFA.2016.7733503.Google Scholar
Gruber, T.R. (1993), “A translation approach to portable ontology specifications”, Knowledge Acquisition, Vol. 5 No. 2, pp. 199–220. http://doi.org/10.1006/knac.1993.1008.Google Scholar
Harding, J.A., Shahbaz, M. and Kusiak, A. (2006), “Data mining in manufacturing. A review”, Journal of Manufacturing Science and Engineering, Vol. 128 No. 4, pp. 969–976.Google Scholar
Hitzler, P., Krötzsch, M., Rudolph, S. and Sure, Y. (2008), Semantic Web, Springer, Berlin, Heidelberg. http://doi.org/10.1007/978-3-540-33994-6.Google Scholar
Horridge, M. and Patel-Schneider, P.F. (2012), “OWL 2 Web Ontology Language”, Manchester Syntax (2nd ed.). [online] W3C, Available at: https://www.w3.org/TR/2012/NOTE-owl2-manchester-syntax-20121211/ (accessed 19 February 2018).Google Scholar
Ishino, Y. and Jin, Y. (2001), “Data mining for knowledge acquisition in engineering design”, In Data mining for design and manufacturing, Springer, Berlin, Heidelberg, pp. 145–160.10.1007/978-1-4757-4911-3_6Google Scholar
Kemper, H.-G., Mehanna, W. and Unger, C. (2006), Business Intelligence - Grundlagen und praktische Anwendungen: Eine Einführung in die IT-basierte Managementunterstützung, IT erfolgreich lernen, 2nd ed., Vieweg and Sohn Verlag, Wiesbaden. http://doi.org/10.1007/978-3-8348-9056-6.Google Scholar
Kestel, P., Luft, T., Schon, C., Kügler, P., Bayer, T., Schleich, B. and Wartzack, S. (2017), “Konzept zur zielgerichteten, ontologiebasierten Wiederverwendung von Produktmodellen”. Design for X, 04.-05.10.2017, TuTech, Hamburg, pp. 241–252.Google Scholar
Kügler, P., Kestel, P., Schon, C., Marian, M., Schleich, B., Staab, S. and Wartzack, S. (2018) “Ontology-based approach for the use of the intentional forgetting in product deveopment”, In Proceedings of the DESIGN, pp. 1595–1606. http://doi.org/10.21278/idc.2018.0402.Google Scholar
Küstner, C. and Wartzack, S. (2015), “The realization of an engineering assistance system for the development of noise-reduced rotating machines”. 20th International Conference on Engineering Design, Milan, Italy, 27.-30.07.2015, Design Society, Glasgow, UK, pp. 71–80.Google Scholar
Le, Q. and Panchal, J.H. (2012), “Analysis of the interdependent co-evolution of product structures and community structures using dependency modelling techniques”, Journal of Engineering Design, Vol. 23 No. 10-11, pp. 807–828.Google Scholar
Matt, C., Hess, T. and Benlian, A. (2015), “Digital Transformation Strategies”, Business and Information Systems Engineering, Vol. 57 No. 5, pp. 339–343. http://doi.org/10.1007/s12599-015-0401-5.Google Scholar
Minsky, M. (1988), “A framework for representing knowledge”, In: Haugeland, J. (Ed.), Mind design: Philosophy, psychology, artificial, 6th ed., MIT Press, Cambridge.Google Scholar
Otte, R., Otte, V. and Kaiser, V. (2004), Data Mining für die industrielle Praxis, Hanser, München.Google Scholar
Parraguez, P. and Maier, A. (2018), “Data-driven engineering design research. Opportunities using open data”. 21st International Conference on Engineering Design, Vancouver, Canada, 21-25.08., Design Society, Glasgow, UK, pp. 41–50.Google Scholar
Quillian, M.R. (1967), “Word concepts. A theory and simulation of some basic semantic capabilities”, Behavioral science, Vol. 12 No. 5, pp. 410–430. http://doi.org/10.1002/bs.3830120511.Google Scholar
Spruegel, T.C. and Wartzack, S. (2015), “Concept and application of automatic part-recognition with artificial neural networks for FE simulations”. 20th International Conference on Engineering Design, Milan, Italy, 27.-30.07.2015, Design Society, Glasgow, UK, pp. 183–194.Google Scholar
Staab, S. and Studer, R. (2009), Handbook on Ontologies, International Handbooks on Information Systems, 2nd ed., Springer, Berlin, Heidelberg. http://doi.org/10.1007/978-3-540-92673-3.Google Scholar
Tan, P.-N., Steinbach, M. and Kumar, V. (2010), Introduction to data mining, int. ed., Pearson/Addison-Wesley, Boston, Mass.Google Scholar
van Ruijven, L.C. (2013), “Ontology for Systems Engineering”, Procedia Computer Science, Vol. 16, pp. 383–392. http://doi.org/10.1016/j.procs.2013.01.040.Google Scholar