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research-article

The development of an ontology for describing the capabilities of manufacturing resources

Authors:

Eeva Järvenpää,

Minna LanzAuthors Info & Claims

Journal of Intelligent Manufacturing, Volume 30, Issue 2

Pages 959 - 978

https://doi.org/10.1007/s10845-018-1427-6

Published: 01 February 2019 Publication History

Abstract

Today’s highly volatile production environments call for adaptive and rapidly responding production systems that can adjust to the required changes in processing functions, production capacity and dispatching of orders. There is a desire to support such system adaptation and reconfiguration with computer-aided decision support systems. In order to bring automation to reconfiguration decision making in a multi-vendor resource environment, a common formal resource model, representing the functionalities and constraints of the resources, is required. This paper presents the systematic development process of an OWL-based manufacturing resource capability ontology (MaRCO), which has been developed to describe the capabilities of manufacturing resources. As opposed to other existing resource description models, MaRCO supports the representation and automatic inference of combined capabilities from the representation of the simple capabilities of co-operating resources. Resource vendors may utilize MaRCO to describe the functionality of their offerings in a comparable manner, while the system integrators and end users may use these descriptions for the fast identification of candidate resources and resource combinations for a specific production need. This article presents the step-by-step development process of the ontology by following the five phases of the ontology engineering methodology: feasibility study, kickoff, refinement, evaluation, and usage and evolution. Furthermore, it provides details of the model’s content and structure.

References

[1]

Ameri, F., & Dutta, D. (2006). An upper ontology for manufacturing service description. In ASME conference proceedings (pp. 651–661).

[2]

Ameri F and Patil L Digital manufacturing market: A semantic web-based framework for agile supply chain deployment Journal of Intelligent Manufacturing 2012 23 5 1817-1832

[3]

Ameri, F., Urbanovsky, C., & McArthur, C. (2012). A systematic approach to developing ontologies for manufacturing service modeling. In Proceedings of the workshop on ontology and semantic web for manufacturing, 2012.

[4]

Backhaus J and Reinhart G Digital description of products, processes and resources for task-oriented programming of assembly systems Journal of Intelligent Manufacturing 2017 28 8 1787-1800

[5]

Baheti, R., & Gill, H. (2011). Cyber-physical systems. In The impact of control technology (pp. 161–166).

[6]

Barata J, Camarinhamatos L, and Candido G A multiagent-based control system applied to an educational shop floor Robotics and Computer-Integrated Manufacturing 2008 24 5 597-605

[7]

Bengel, M. (2007). Modelling objects for skill-based reconfigurable machines. In Innovative production machines and systems, 3rd I*PROMS virtual international conference 2007 (p. 13).

[8]

Borgo, S., & Leitão, P. (2007). Foundations for a core ontology of manufacturing. In Integrated series in information systems (Vol. 14).

[9]

Chapurlat V, Diep D, Kalogeras A, and Gialelis J Gonçalves RJ, Müller JP, Mertins K, and Zelm M Building and validating a manufacturing ontology to achieve interoperability Enterprise interoperability II 2007 London Springer

[10]

ElMaraghy HA Flexible and reconfigurable manufacturing systems paradigms International Journal of Flexible Manufacturing Systems 2006 17 4 261-276

[11]

Frei, R., Di Marzo Serugendo, G., Pereira, N., Belo, J., & Barata, J. (2010). Implementing self-organisation and self-management in evolvable assembly systems. In IEEE international symposium on industrial electronics (pp. 3527–3532).

[12]

Gruber TA Translation approach to portable ontology specification Knowledge Acquisition 1993 5 2 199-220

[13]

Gruninger M and Menzel C The process specification language (PSL) theory and applications AI Magazine 2003 24 3 63-74

[14]

Guarino N and Welty CA Staab S and Studer R An overview of OntoClean Handbook on ontologies 2009 2 New York Springer 201-220

[15]

Horrocks, I., et al. (2004) SWRL: A semantic web rule language—Combining OWL and RuleML. W3C member submission. http://www.w3.org/Submission/SWRL/. Accessed March 10, 2016.

[16]

Hu Y, Tao F, Zhao D, and Zhou S Manufacturing grid resource and resource service digital description The International Journal of Advanced Manufacturing Technology 2009 44 9–10 1024-1035

[17]

Jardim-Goncalves R, Sarraipa J, Agostinho C, and Panetto H Knowledge framework for intelligent manufacturing systems Journal of Intelligent Manufacturing 2011 22 5 725-735

[18]

Järvenpää E, Luostarinen P, Lanz M, and Tuokko R Presenting capabilities of resources and resource combinations to support production system adaptation IEEE International Symposium on Assembly and Manufacturing (ISAM) 2011 2011 6

[19]

Järvenpää, E. (2012) Capability-based adaptation of production systems in a changing environment. Ph.D. thesis, Tampere University of Technology.

[20]

Järvenpää, E., Siltala, N., & Lanz, M. (2016). Formal resource and capability descriptions supporting rapid reconfiguration of assembly systems. In Proceedings of the 12th conference on automation science and engineering, and international symposium on assembly and manufacturing (pp. 120–125). IEEE.

[21]

Järvenpää E, Siltala N, Hylli O, and Lanz M Capability matchmaking procedure to support rapid configuration and re-configuration of production systems Procedia Manufacturing 2017 19 87-94

[22]

Järvenpää, E., Hylli, O., Siltala, N., & Lanz, M. (2018a). Utilizing SPIN rules to infer the parameters for combined capabilities of aggregated manufacturing resources. In 16th IFAC symposium on information control problems in manufacturing, 11–13 June 2018, Bergamo, Italy.

[23]

Järvenpää, E. Siltala, N., Hylli, O., & Lanz, M. (2018b). Product model ontology and its use in capability-based matchmaking. In 51st CIRP conference on manufacturing systems, 16–18 May 2018, Stockholm, Sweden.

[24]

Kitamura Y, Koji Y, and Mizoguchi R An ontological model of device function: Industrial deployment and lessons learned Applied Ontology 2006 1 3–3 237-262

[25]

Knublauch, H. (2016). The TopBraid SPIN API. http://topbraid.org/spin/api/. Accessed April 1, 2017.

[26]

Koren Y and Shpitalni M Design of reconfigurable manufacturing systems Journal of Manufacturing Systems 2010 29 4 130-141

[27]

Leitão P, Colombo AW, and Karnouskos S Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges Computers in Industry 2016 81 11-25

[28]

Lemaignan S, Siadat A, Dantan J-Y, and Semenenko A MASON: A proposal for an ontology of manufacturing domain 2006 Prague Institute of Electrical and Electronics Engineers Computer Society

[29]

Lohse, N. (2006). Towards an ontology framework for the integrated design of modular assembly systems. Ph.D. thesis, University of Nottingham (p. 245).

[30]

Malec, J., Nilsson, A., Nilsson, K., & Nowaczyk, S. (2007). Knowledge-based reconfiguration of automation systems. In 3rd annual IEEE conference on automation science and engineering, 2007 (pp. 170–175).

[31]

Matsokis A and Kiritsis D An ontology-based approach for Product Lifecycle Management Computers in Industry 2010 61 8 787-797

[32]

Mehrabi MG, Ulsoy AG, and Koren Y Reconfigurable manufacturing systems: Key to future manufacturing Journal of Intelligent Manufacturing 2000 11 4 403-419

[33]

Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Technical report SMI-2001-0880, Stanford Medical Informatics.

[34]

Obitko M, Vrba P, Marik V, Radakovic M, and Kadera P Applications of semantics in agent-based manufacturing systems Informatica 2010 34 315-330

[35]

Pfrommer, J., Stogl, D., Aleksandrov, K., Schubert, V., & Hein, B. (2014). Modelling and orchestration of service-based manufacturing systems via skills. In Proceedings of the IEEE emerging technology and factory automation, 2014.

[36]

Protégé. (2015). Protégé Ontology Editor web page. http://protege.stanford.edu/. Accessed November 1, 2015.

[37]

Rauschecker, U., & Stöhr, M. (2012). Using manufacturing service descriptions for flexible integration of production facilities to manufacturing clouds. In Proceedings of the 18th international conference on engineering technology and innovation, 2012.

[38]

Ray S and Jones AT Manufacturing interoperability Journal of Intelligent Manufacturing 2006 17 681-688

[39]

Salonen, J., Nykanen O, Ranta P. A., et al. (2011). An implementation of a semantic, web-based virtual machine laboratory prototyping environment. In Aroyo, L., et al. (Eds.), The semantic web ISWC 2011 10th international semantic web conference, proceedings, Part II. LNCS (Vol. 7032, pp. 221–236).

[40]

Shin J, Kulvatunyou B, Lee Y, et al. Concept analysis to enrich manufacturing service capability models Procedia Computer Science 2013 16 648-657

[41]

Siltala N, Järvenpää E, Lanz M, et al. Nääs I et al. Formal information model for representing production resources Advances in production management systems. Initiatives for a sustainable world. APMS 2016. IFIP advances in information and communication technology 2016 Cham Springer

[42]

Siltala, N., Järvenpää, E., & Lanz, M. (2018). Creating resource combinations based on formally described hardware interfaces. In Eight international precision assembly seminar, IPAS (unpublished).

[43]

Sintec, M. (2007). OntoViz homepage. https://protegewiki.stanford.edu/wiki/OntoViz. Accessed August 9, 2017.

[44]

Sirin E, Parsia P Cuenca, Grau B, Kalyanpur A, and Katz Y Pellet: A practical OWL-DL reasoner Journal of Web Semantics 2007 5 2 51-53

[45]

SPIN Working Group. (2017). SPIN—SPARQL inferencing notation. http://spinrdf.org/. Accessed October 15, 2017.

[46]

Staab S, Studer R, Schnurr H-P, and Sure Y Knowledge processes and ontologies IEEE Intelligent Systems 2001 16 1 26-34

[47]

Strzelczak, S. (2015). Towards ontology-aided manufacturing and supply chain management: A literature review. In Advances in production management systems: Innovative production management towards sustainable growth (pp. 467–475).

[48]

Studer R, Benjamin V, and Fensel D Knowledge engineering: Principles and methods Data and Knowledge Engineering 1998 25 161-197

[49]

Sugumaran V and Storey VC Ontologies for conceptual modeling: Their creation, use and management Data and Knowledge Engineering 2002 42 251-271

[50]

Sure Y, Staab S, and Studer R Staab S and Studer R Ontology engineering methodology Handbook on ontologies 2009 2 New York Springer 135-152

[51]

Terkaj, W., & Urgo, M. (2012). Virtual factory data model to support performance evaluation of production systems. In CEUR workshop proceedings (Vol. 886, pp. 44–58).

[52]

Thoben K-D, Wiesner S, and Wuest T ”Industrie 4.0” and smart manufacturing: A review of research issues and application examples International Journal of Automation Technology 2017 11 1 4-16

[53]

Timm IJ, Scholz T, and Herzog O Capability-based emerging organization of autonomous agents for flexible production control Advanced Engineering Informatics 2006 20 3 247-259

[54]

Uschold M and Gruninger M Ontologies: Principles, methods and applications Knowledge Engineering Review 1996 11 2 1-63

[55]

Westkämper E Daschenko A Factory transformability: Adapting the structures of manufacturing Reconfigurable manufacturing systems and transformable factories 2006 Berlin Springer 371-381

[56]

W3C. (2004). OWL web ontology language—Reference. http://www.w3.org/TR/owl-ref/. Accessed November 1, 2015.

[57]

W3C. (2008). SPARQL query language for RDF. http://www.w3.org/TR/rdf-sparql-query/. Accessed March 10, 2016.

[58]

Yao, X., Zhou, J., Lin, Y., Li, Y., Yu, H., & Liu, Y. (2017). Smart manufacturing based on cyber-physical systems and beyond. Journal of Intelligent Manufacturing.

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cover image Journal of Intelligent Manufacturing

Journal of Intelligent Manufacturing Volume 30, Issue 2

Feb 2019

504 pages

ISSN:0956-5515

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© The Author(s) 2018.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 February 2019

Accepted: 31 May 2018

Received: 14 November 2017

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Mo FChaplin JSanderson DMartínez-Arellano GRatchev S(2024)Semantic models and knowledge graphs as manufacturing system reconfiguration enablersRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2023.10262586:COnline publication date: 1-Apr-2024
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