Reasoning on Engineering Knowledge: Applications and Desired Features

  • Constantin Hildebrandt
  • Matthias Glawe
  • Andreas W. Müller
  • Alexander Fay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10250)

Abstract

The development and operation of highly flexible automated systems for discrete manufacturing, which can quickly adapt to changing products, has become a major research field in industrial automation. Adapting a manufacturing system to a new product for instance requires comparing the systems functionality against the requirements imposed by the changed product. With an increasing frequency of product changes, this comparison should be automated. Unfortunately, there is no standard way to model the functionality of a manufacturing system, which is an obstacle to automation. The engineer still has to analyze all documents provided by engineering tools like 3D-CAD data, electrical CAD data or controller code. In order to support this time consuming process, it is necessary to model the so-called skills of a manufacturing system. A skill represents certain features an engineer has to check during the adaption of a manufacturing system, e.g. the kinematic of an assembly or the maximum load for a gripper. Semantic Web Technologies (SWT) provide a feasible solution for modeling and reasoning on the knowledge of these features. This paper provides the results of a project that focused on modeling the kinematic skills of assemblies. The overall approach as well as further requirements are shown. Since not all expectations on reasoning functionality could be met by available reasoners, the paper focuses on desired reasoning features that would support the further use of SWT in the engineering domain.

References

  1. 1.
    Wiendahl, H.-P., ElMaraghy, H.A., Nyhuis, P., Zäh, M.F., Wiendahl, H.-H., Duffie, N., Brieke, M.: Changeable manufacturing - classification, design and operation. CIRP Ann. Manufact. Technol. 56(2), 783–809 (2007). doi:10.1016/j.cirp.2007.10.003 CrossRefGoogle Scholar
  2. 2.
    Hu, S.J., Zhu, X., Wang, H., Koren, Y.: Product variety and manufacturing complexity in assembly systems and supply chains. CIRP Ann. Manufact. Technol. 57(1), 45–48 (2008). doi:10.1016/j.cirp.2008.03.138 CrossRefGoogle Scholar
  3. 3.
    Mourtzis, D., Doukas, M.: Decentralized manufacturing systems review challenges and outlook. Logist. Res. 5(3–4), 113–121 (2012). doi:10.1007/s12159-012-0085-x CrossRefGoogle Scholar
  4. 4.
    Vogel-Heuser, B., Diedrich, C., Fay, A., Jeschke, S., Kowalewski, S., Wollschlaeger, M., Göhner, P.: Challenges for software engineering in automation. JSEA 07(05), 440–451 (2014). doi:10.4236/jsea.2014.75041 CrossRefGoogle Scholar
  5. 5.
    Strube, M., Runde, S., Figalist, H., Fay, A.: Risk minimization in modernization projects of plant automation — a knowledge-based approach by means of semantic web technologies. In: Factory Automation (ETFA 2011), Toulouse, France, pp. 1–8 (2011)Google Scholar
  6. 6.
    Runde, S., Fay, A.: Software support for building automation requirements engineering—an application of semantic web technologies in automation. IEEE Trans. Ind. Inf. 7(4), 723–730 (2011). doi:10.1109/TII.2011.2166784 CrossRefGoogle Scholar
  7. 7.
    Legat, C.: Knowledge-based technologies for future factory engineering and control. IFAC Proc. Volumes 45(6), 44–48 (2012). doi:10.3182/20120523-3-RO-2023.00447 CrossRefGoogle Scholar
  8. 8.
    Puttonen, J., Lobov, A., Lastra, M.: Semantics-based composition of factory automation processes encapsulated by web services. IEEE Trans. Ind. Inf. 9(4), 2349–2359 (2013). doi:10.1109/TII.2012.2220554 CrossRefGoogle Scholar
  9. 9.
    Legat, C., Schütz, D., Vogel-Heuser, B.: Automatic generation of field control strategies for supporting (re-)engineering of manufacturing systems. J. Intell. Manuf. 25(5), 1101–1111 (2014). doi:10.1007/s10845-013-0744-z CrossRefGoogle Scholar
  10. 10.
    Harcuba, O., Vrba, P.: Ontologies for flexible production systems. In: 20th Conference on Emerging Technologies and Factory Automation (ETFA). International Conference on Emerging Technologies & Factory Automation,. Luxembourg, Luxembourg. IEEE, Institute of Electrical and Electronics Engineers, Piscataway (2015)Google Scholar
  11. 11.
    Bunte, A., Diedrich, A., Niggemann, O.: Integrating semantics for diagnosis of manufacturing systems. In: 21st IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Berlin, Germany, 6–9 September 2016. IEEE, Institute of Electrical and Electronics Engineers (2016)Google Scholar
  12. 12.
    Aarnio, P., Vyatkin, V., Hästbacka, D.: Context modeling with situation rules for industrial maintenance. In: 21st IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. Berlin, Germany, 6–9 September 2016. IEEE, Institute of Electrical and Electronics Engineers (2016)Google Scholar
  13. 13.
    Glawe, M., Tebbe, C., Fay, A., Niemann, K.-H.: Knowledge-based engineering of automation systems using ontologies and engineering data. In: 7th International Conference on Knowledge Engineering and Ontology Development, Lisbon, Portugal, 12–14 November 2015Google Scholar
  14. 14.
    Abele, L., Legat, C., Grimm, S., Muller, A.W.: Ontology-based validation of plant models. In: IEEE 11th International Conference on Industrial Informatics (INDIN), Bochum, Germany, pp. 236–241 (2015)Google Scholar
  15. 15.
    Ramis Ferrer, B., Ahmad, B., Vera, D., Lobov, A., Harrison, R., Martínez Lastra, J.L.: Product, process and resource model coupling for knowledge-driven assembly automation. Automatisierungstechnik 64(3), 231–243 (2016). doi:10.1515/auto-2015-0073 CrossRefGoogle Scholar
  16. 16.
    Negri, E., Fumagalli, L., Garetti, M., Tanca, L.: Requirements and languages for the semantic representation of manufacturing systems. Comput. Ind. 81, 55–66 (2016). doi:10.1016/j.compind.2015.10.009 CrossRefGoogle Scholar
  17. 17.
    Pfrommer, J., Stogl, D., Aleksandrov, K., Escaida Navarro, S., Hein, B., Beyerer, J.: Plug & produce by modelling skills and service-oriented orchestration of reconfigurable manufacturing systems. Automatisierungstechnik 63(10), 790–800 (2015). doi:10.1515/auto-2014-1157 CrossRefGoogle Scholar
  18. 18.
    Usländer, T., Epple, U.: Reference model of industrie 4.0 service architectures. Automatisierungstechnik 63(10), 858–866 (2015). doi:10.1515/auto-2015-0017 CrossRefGoogle Scholar
  19. 19.
    Industrial Internet Consortium: Industrial Internet Reference Architecture (2015). https://www.iiconsortium.org/IIRA-1-7-ajs.pdf
  20. 20.
    Jules, G.D., Saadat, M., Li, N.: On designing a unified ontology for holonic manufacturing networks. In: Fathi, M. (ed.) Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives, pp. 207–220. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  21. 21.
    Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: a semantic web rule language combining OWL and ruleML. In: W3C-World Wide Web Consortium. https://www.w3.org/Submission/SWRL/#8, zuletzt geprüft am 16 September 2016
  22. 22.
    Roda, F., Zanni-Merk, C.: An intelligent data analysis framework for supporting perception of geospatial phenomena. In: Ferrario, R., Kuhn, W. (eds.) Formal ontology in information systems. In: Proceedings of the 9th International Conference (FOIS 2016). Frontiers in artificial intelligence and applications, vol. 283. IOS Press, Amsterdam (2016)Google Scholar
  23. 23.
    Matentzoglu, N., Leo, J., Hudhra, V., Parsia, B., Sattler, U.: A survey of current, stand-alone OWL Reasoners. In: Informal Proceedings of the 4th International Workshop on OWL Reasoner Evaluation (2015)Google Scholar
  24. 24.
    Pahl, G., Beitz, W., Blessing, L., Feldhusen, J., Grote, K.-H., Wallace, K. (eds.): Engineering Design. A Systematic Approach, 3rd edn. Springer, London (2007). http://site.ebrary.com/lib/alltitles/docDetail.action?docID=10230457 Google Scholar
  25. 25.
    Basseda, R., Gao, T., Kifer, M., Greenspan, S., Chell, C.: Representing flexible role-based access control policies using objects and defeasible reasoning. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds.) RuleML 2015. LNCS, vol. 9202, pp. 376–387. Springer, Cham (2015). doi:10.1007/978-3-319-21542-6_24 CrossRefGoogle Scholar
  26. 26.
    Rakib, A., Haque, H.M.U.: Modeling and verifying context-aware non-monotonic reasoning agents. In: ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE), Austin, TX, USA, pp. 61–69 (2015)Google Scholar
  27. 27.
    Casini, G., Meyer, T., Moodley, K., Sattler, U., Varzinczak, I.: Introducing defeasibility into OWL ontologies. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 409–426. Springer, Cham (2015). doi:10.1007/978-3-319-25010-6_27 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Constantin Hildebrandt
    • 1
  • Matthias Glawe
    • 1
  • Andreas W. Müller
    • 2
  • Alexander Fay
    • 1
  1. 1.Institute of Automation TechnologyHelmut-Schmidt-UniversityHamburgGermany
  2. 2.Data Architecture and FrameworksSchaeffler Technologies AG & Co. KGHerzogenaurachGermany

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