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Product family design knowledge representation, aggregation, reuse, and analysis

Published online by Cambridge University Press:  19 March 2007

JYOTIRMAYA NANDA ,
HENRI J. THEVENOT ,
TIMOTHY W. SIMPSON ,
ROBERT B. STONE ,
MATT BOHM  and
STEVEN B. SHOOTER
JYOTIRMAYA NANDA
Affiliation:
Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, Pennsylvania, USA
HENRI J. THEVENOT
Affiliation:
Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, Pennsylvania, USA
TIMOTHY W. SIMPSON
Affiliation:
Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, Pennsylvania, USA
ROBERT B. STONE
Affiliation:
Department of Interdisciplinary Engineering, University of Missouri–Rolla, Rolla, Missouri, USA
MATT BOHM
Affiliation:
Department of Interdisciplinary Engineering, University of Missouri–Rolla, Rolla, Missouri, USA
STEVEN B. SHOOTER
Affiliation:
Department of Mechanical Engineering, Bucknell University, Lewisburg, Pennsylvania, USA
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Abstract

A flexible information model for systematic development and deployment of product families during all phases of the product realization process is crucial for product-oriented organizations. In current practice, information captured while designing products in a family is often incomplete, unstructured, and is mostly proprietary in nature, making it difficult to index, search, refine, reuse, distribute, browse, aggregate, and analyze knowledge across heterogeneous organizational information systems. To this end, we propose a flexible knowledge management framework to capture, reorganize, and convert both linguistic and parametric product family design information into a unified network, which is called a networked bill of material (NBOM) using formal concept analysis (FCA); encode the NBOM as a cyclic, labeled graph using the Web Ontology Language (OWL) that designers can use to explore, search, and aggregate design information across different phases of product design as well as across multiple products in a product family; and analyze the set of products in a product family based on both linguistic and parametric information. As part of the knowledge management framework, a PostgreSQL database schema has been formulated to serve as a central design repository of product design knowledge, capable of housing the instances of the NBOM. Ontologies encoding the NBOM are utilized as a metalayer in the database schema to connect the design artifacts as part of a graph structure. Representing product families by preconceived common ontologies shows promise in promoting component sharing, and assisting designers search, explore, and analyze linguistic and parametric product family design information. An example involving a family of seven one-time-use cameras with different functions that satisfy a variety of customer needs is presented to demonstrate the implementation of the proposed framework.

Keywords

Design RepositoryInformation ManagementOntologyProduct Family
Type
Research Article
Information
AI EDAM , Volume 21 , Issue 2 , April 2007 , pp. 173 - 192
Copyright
© 2007 Cambridge University Press

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References

REFERENCES

Baader, F., Calvanese, D., McGuinness, D., Nardi, D., & Patel-Schneider, P. (2003). The Description Logic Handbook: Theory, Implementation and Applications. Cambridge: Cambridge University Press.
Becker, P. (2004). Numerical analysis in conceptual systems with ToscanaJ. Concept Lattices: Second Int. Conf. Formal Concept Analysis (ICFCA), pp. 96–103, Sydney, Australia.
Bohm, M.R., Stone, R.B., Simpson, T.W., & Steva, E.D. (2006). Introduction of a data schema: the inner workings of a design repository. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2006/CIE-99518, Philadelphia, PA.CrossRef
Bohm, M.R., Stone, R.B., & Szykman, S. (2005). Enhancing virtual product representations for advanced design repository systems. Journal of Computer Information Science in Engineering 5(4), 360–372.CrossRefGoogle Scholar
Boothroyd, G. & Dewhurst, P. (2002). Product Design for Manufacture and Assembly, 2nd ed., rev. New York: Marcel Dekker.
Bourke, R. (1999). Product information management: product lifecycle management in complex industries, MidRange ERP. Accessed at http://industrydirections.com/midrange/rb0499.htm
Burmeister, P. (2003). Formal Concept Analysis With CONIMP: Introduction to the Basic Features. Darmstadt, Germany: Darmstadt University of Technology, Department of Mathematics.
Clark, K.G. (2005). SPARQL protocol for RDF. World Wide Web Consortium. Accessed at http://www.w3.org/TR/2005/WD-rdf-sparql-protocol-20050527/
Collier, D.A. (1981). The measurement and operating benefits of component part commonality. Decision Sciences 12(1), 85–96.CrossRefGoogle Scholar
Daconta, M.C., Obrst, L.J., & Smith, K.T. (2003). The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management. Indianapolis, IN: Wiley.
Davies, J., Fensel, D., & van Harmelen, F. (2003). Towards the Semantic Web: Ontology-Driven Knowledge Management. West Sussex: Wiley.
Denny, M. (2004). Ontology Tools Survey, Revisited. O'Reilly. XML.com. Accessed at http://www.xml.com/pub/a/2004/07/14/onto.html
Dixon, J.R. & Poli, C. (1999). Engineering design & design for manufacturing: A structured approach. Conway, MA: Field Stone Publishers.
Douglas, K. & Douglas, S. (2003). PostgreSQL, 1st ed. Indianapolis, IN: Sams.
Duquenne, V., Chabert, C., Cherfouh, A., Delabar, J.-M., Doyen, A.-L., & Pickering, D. (2001). Structuration of phenotypes/genotypes through Galois lattices and implications. Int. Workshop on Concept Lattice-Based Theory, Methods and Tools for Knowledge Discovery in Databases, Stanford University, Stanford, CA.
Erné, M., Koslowski, J., Melton, A., & Strecker, G.E. (1991). A primer on Galois connections. Proc. Summer Conf. General Topology and Applications in Honor of Mary Ellen Rudin and Her Work, pp. 103–125, Madison, WI.
Fernandez-Manjon, B. & Fernandez-Valmayor, A. (1998). Building educational tools based on formal concept analysis. Education and Information Technologies 3(3), 187–201.CrossRefGoogle Scholar
Fikes, R., Hayes, P., & Horrocks, I. (2003). OWL-QL—A Language for Deductive Query Answering on the Semantic Web. Palo Alto, CA: Stanford University, Knowledge Systems Laboratory.
Ganter, B. & Wille, R. (1999). Formal Concept Analysis: Mathematical Foundations. Heidelberg: Springer–Verlag.CrossRef
Gennari, J.H., Tu, S.W., Rothenfluh, T.E., & Musen, M.A. (1994). Mapping domains to methods in support of reuse. International Journal of Human–Computer Studies 41(3), 399–424.CrossRefGoogle Scholar
Godin, R. & Mili, H. (1993). Building and maintaining analysis-level class hierarchies using Galois lattices. Proc. Eighth Annual Conf. Object-Oriented Programming Systems, Languages, and Applications, pp. 394–410, Washington, DC.
Godin, R., Missaoui, R., & April, A. (1993). Experimental comparison of navigation in a galois lattice with conventional information retrieval methods. International Journal of Man–Machine Studies 38(5), 747–767.CrossRefGoogle Scholar
Gratzer, G.A. (1998). General Lattice Theory. Boston: Birkhäuser.
Groh, B., Strahringer, S., & Wille, R. (1998). Toscana-systems based on thesauri, conceptual structures: theory, tools and applications. 6th Int. Conf. Conceptual Structures (ICCS'98), pp. 127–138, Montpellier, France.
Hatvany, J., Newman, W.M., & Sabin, M.A. (1993). World survey of computer-aided design. Computer Aided Design 25(12), 776–798.CrossRefGoogle Scholar
Hirtz, J., Stone, R., McAdams, D., Szykman, S., & Wood, K. (2002). A functional basis for engineering design: reconciling and evolving previous efforts. Research in Engineering Design 13(2), 65–82.CrossRefGoogle Scholar
Iwasaki, Y. & Chandrasekaran, B. (1992). Design verification through function and behavior-oriented representations: bridging the gap between function and behavior. Second Int. Conf. Artificial Intelligence in Design, Pittsburgh, PA.
Kirschman, C. & Fadel, G.M. (1998). Classifying functions for mechanical design. ASME Journal of Mechanical Design 120(3), 475–482.CrossRefGoogle Scholar
Koivunen, M.-R. & Miller, E. (2001). W3C Semantic Web activity. World Wide Web Consortium. Accessed at http://www.w3.org/2001/12/semweb-fin/w3csw
Kota, S., Sethuraman, K., & Miller, R. (2000). A metric for evaluating design commonality in product families. ASME Journal of Mechanical Design 122(4), 403–410.CrossRefGoogle Scholar
Kurtoglu, T., Campbell, M.I., Bryant, C.R., Stone, R.B., & McAdams, D.A. (2005). Deriving a component basis for computational functional synthesis. Int. Conf. Engineering Design (ICED05), Melbourne, Australia.
Kuznetsov, S.O. (2001). Machine learning on the basis of formal concept analysis. Automation and Remote Control 62(10), 1543–1564.CrossRefGoogle Scholar
Liao, S.-H. (2003). Knowledge management technologies and applications—literature review from 1995 to 2002. Expert Systems with Applications 25(2), 155–164.CrossRefGoogle Scholar
Lindig, C. (1995). Concept-based component retrieval. Int. Joint Conf. Artificial Intelligence: Formal Approaches to the Reuse of Plans, Proofs, and Programs (IJCAI-95), Montreal, Canada.
Martin, M.V. & Ishii, K. (1997). Design for variety: development of complexity indices and design charts. 1997 ASME Design Engineering Technical Conf. Design for Manufacturability, Paper No. DETC97/DFM-4359, Sacramento, CA.
McBride, B. (2002). Jena: a semantic web toolkit. In IEEE Internet Computing, pp. 55–59.CrossRef
McGuinness, D.L. & van Harmelen, F. (2004). OWL web ontology language overview. Recommendation. World Wide Web Consortium. Accessed at http://www.w3.org/TR/2004/REC-owl-features-20040210/
Miller, L., Seaborne, A., & Reggiori, A. (2002). Three Implementations of SQUISHQL. A Simple RDF Query Language. Bristol: Hewlett–Packard, Information Infrastructure Laboratory.
Murdock, J.W., Szykman, S., & Sriram, R.D. (1997). An information modeling framework to support design databases and repositories. ASME Design Engineering Technical Conf., Paper No. DETC97/DFM-4373, Sacramento, CA.
Nanda, J., Simpson, T.W., Shooter, S.B., & Stone, R.B. (2005a). A unified information model for product family design management. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Long Beach, CA.
Nanda, J., Thevenot, H., & Simpson, T.W. (2005b). Product family representation and redesign: increasing commonality using formal concept analysis. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2005/DAC84818, Long Beach, CA.
Nanda, J., Thevenot, H.J., & Simpson, T.W. (2005c). Product family design knowledge representation, integration, and reuse. IEEE Int. Conf. Information Reuse and Integration, pp. 32–37, Las Vegas, NV.
Nanda, J., Simpson, T.W., Kumara, S.R.T., & Shooter, S.B. (2006). Product family ontology development using formal concept analysis and web ontology language. ASME Journal of Computing and Information Science in Engineering 6(1), 103–113.CrossRefGoogle Scholar
Nourine, L. & Raynaud, O. (1999). Fast algorithm for building lattices. Information Processing Letters 71(5–6), 199–204.CrossRefGoogle Scholar
Noy, N.F., Sintek, M., Decker, S., Crubézy, M., Fergerson, R.W., & Musen, M.A. (2001). Creating semantic web contents with Protégé-2000. IEEE Intelligent Systems 16(2), 60–71.CrossRefGoogle Scholar
Otto, K.N. & Wood, K.L. (2001). Product Design: Techniques in Reverse Engineering and New Product Development. Upper Saddle River, NJ: Prentice–Hall.
Priss, U. (2003). Linguistic applications of formal concept analysis. Proc. First Int. Conf. Formal Concept Analysis (ICFCA'03), Darmstadt, Germany.
Saaksvuori, A. & Immonen, A. (2003). Product Lifecycle Management. Heidelberg: Springer–Verlag.
Shooter, S.B., Keirouz, W.T., Szykman, S., & Fenves, S.J. (2000). A model for the flow of design information in product development. Journal of Engineering with Computers 16(3–4), 178–194.CrossRefGoogle Scholar
Shooter, S.B., Simpson, T.W., Kumara, S.R.T., Stone, R.B., & and Terpenny, J.P. (2005). Toward a multi-agent information infrastructure for product family planning and mass customization. International Journal of Mass Customization 1(1), 134–155.CrossRefGoogle Scholar
Siff, M. & Reps, T. (1997). Identifying modules via concept analysis. IEEE Int. Conf. Software Maintenance, pp. 170–179, Bari, Italy.CrossRef
Simpson, T.W. (2004). Product platform design and customization: status and promise. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18(1), 3–20.Google Scholar
Szykman, S., Racz, J., Bochenek, C., & Sriram, R.D. (2000). Web-based system for design artifact modeling. Design Studies 21(2), 145–165.CrossRefGoogle Scholar
Thevenot, H.J. & Simpson, T.W. (2006a). Commonality indices for product family design: a detailed comparison. Journal of Engineering Design 17(2), 99–119.Google Scholar
Thevenot, H.J. & Simpson, T.W. (2006b). A comprehensive metric for evaluating commonality in a product family. ASME Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2006-DAC99268, Philadelphia, PA.
Tilley, T. (2004). Tool support for FCA, concept lattices. Second Int. Conf. Formal Concept Analysis (ICFCA), pp. 104–111, Sydney, Australia.
Ulrich, K.T. & Eppinger, S.D. (2004). Product Design and Development. New York: McGraw–Hill/Irwin.
Valtchev, P., Missaoui, R., & Lebrun, P. (2002). A partition-based approach towards constructing Galois (concept) lattices. Discrete Mathematics 256(3), 801–829.CrossRefGoogle Scholar
van der Vegte, W.F., Kitamura, Y., Mizoguchi, R., & Horváth, I. (2002). Ontology-based modeling of product functionality and use—part 2: considering use and unintended behavior. Proc. EdiPROD Conf., pp. 115–124.
Vogt, F. & Wille, R. (1995). Toscana—a graphical tool for analyzing and exploring data. Proc. DIMACS Int. Workshop on Graph Drawing, pp. 226–233, Princeton, NJ.
Wacker, J.G. & Trelevan, M. (1986). Component part standardization: an analysis of commonality sources and indices. Journal of Operations Management 6(2), 219–244.CrossRefGoogle Scholar