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Ontology-based executable design decision template representation and reuse

Published online by Cambridge University Press:  04 October 2016

Zhenjun Ming ,
Yan Yan ,
Guoxin Wang ,
Jitesh H. Panchal ,
Chung-Hyun Goh ,
Janet K. Allen  and
Farrokh Mistree
Zhenjun Ming
Affiliation:
Beijing Institute of Technology, Beijing, China
Yan Yan
Affiliation:
Beijing Institute of Technology, Beijing, China
Guoxin Wang
Affiliation:
Beijing Institute of Technology, Beijing, China
Jitesh H. Panchal
Affiliation:
Purdue University, West Lafayette, Indiana, USA
Chung-Hyun Goh
Affiliation:
University of Texas at Tyler, Tyler, Texas, USA
Janet K. Allen*
Affiliation:
School of Industrial and System Engineering, University of Oklahoma, Norman, Oklahoma, USA
Farrokh Mistree
Affiliation:
School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, Oklahoma, USA
*
Reprint requests to: Janet K. Allen, School of Industrial and System Engineering, University of Oklahoma, 202 West Boyd Street, Suite 116, Norman, OK 73019, USA. E-mail: [email protected]
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Abstract

In decision-based design, the principal role of a designer is to make decisions. Decision support is crucial to augment this role. In this paper, we present an ontology that provides decision support from both the “construct” and the “information” perspectives that address the gap that existing research focus on these two perspectives separately and cannot provide effective decision support. The decision support construct in the ontology is the compromise decision support problem (cDSP) that is used to make multiobjective design decisions. The information for decision making is archived as cDSP templates and represented using frame-based ontology for facilitating reuse, consistency maintaining, and rapid execution. In order to facilitate designers’ effective reuse of the populated cDSP templates ontology instances, we identified three types of modification that can be made when design consideration evolves. In our earlier work, part of the utilization (consistency checking) of the ontology has been demonstrated through a thin-walled pressure vessel redesign example. In this paper, we comprehensively present the ontology utilization including consistency checking, trade-off analysis, and design space visualization based on the pressure vessel example.

Keywords

Compromise Decision Support ProblemDecision-Based DesignOntologyReuseTemplate
Type
Special Issue Articles
Information
AI EDAM , Volume 30 , Special Issue 4: Engineering Design Informatics , November 2016 , pp. 390 - 405
Copyright
Copyright © Cambridge University Press 2016 

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References

REFERENCES

Barbau, R., Krima, S., Rachuri, S., Narayanan, A., Fiorentini, X., Foufou, S., & Sriram, R.D. (2012). OntoSTEP: enriching product model data using ontologies. Computer-Aided Design 44(6), 575–590.Google Scholar
Chandrasegaran, S.K., Ramani, K., Sriram, R.D., Horvath, I., Bernard, A., Harik, R.F., & Gao, W. (2013). The evolution, challenges, and future of knowledge representation in product design systems. Computer-Aided Design 45(2), 204–228.Google Scholar
Eriksson, H. (2008). Jess Tab. Accessed at http://www.jessrules.com/jesswiki/view?JessTab on July 15, 2015.Google Scholar
Fenves, S.J., Foufou, S., Bock, C., & Sriram, R.D. (2008). CPM2: a core model for product data. Journal of Computing and Information Science in Engineering 8(1), 014501.Google Scholar
Fernandez, M.G., Seepersad, C.C., Rosen, D.W., Allen, J.K., & Mistree, F. (2005). Decision support in concurrent engineering—the utility-based selection decision support problem. Concurrent Engineering—Research and Applications 13(1), 13–27.CrossRefGoogle Scholar
Gruber, T.R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition 5(2), 199–220.Google Scholar
Gu, X.Y., Renaud, J.E., Ashe, L.M., Batill, S.M., Budhiraja, A.S., & Krajewski, L.J. (2002). Decision-based collaborative optimization. Journal of Mechanical Design 124(1), 1–13.Google Scholar
Hazelrigg, G.A. (1998). A framework for decision-based engineering design. Journal of Mechanical Design 120(4), 653–658.Google Scholar
Kulkarni, N., Gautham, B., Zagade, P., Panchal, J., Allen, J.K., & Mistree, F. (2015). Exploring the geometry and material space in gear design. Engineering Optimization 47(4), 561–577.Google Scholar
Kulok, M., & Lewis, K. (2007). A method to ensure preference consistency in multi-attribute selection decisions. Journal of Mechanical Design 129(10), 1002–1011.CrossRefGoogle Scholar
Lee, J.H., Fenves, S.J., Bock, C., Suh, H.W., Rachuri, S., Fiorentini, X., & Sriram, R.D. (2012). A semantic product modeling framework and its application to behavior evaluation. IEEE Transactions on Automation Science and Engineering 9(1), 110–123.Google Scholar
Lewis, K., & Mistree, F. (1995). Designing top-level aircraft specifications: a decision-based approach to a multiobjective, highly constrained problem. Proc. 36th AIAA/ASME/ ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conf., Paper No. AIAA-95-1431-CP, pp. 2393–2405. New Orleans, LA, April 10–13.Google Scholar
Lewis, K., & Mistree, F. (1998). Collaborative, sequential, and isolated decisions in design. Journal of Mechanical Design 120(4), 643–652.Google Scholar
Lewis, K.E., Chen, W., & Schmidt, L.C. (2006). Decision Making in Engineering Design. New York: ASME Press.CrossRefGoogle Scholar
Li, Z., Raskin, V., & Ramani, K. (2008). Developing engineering ontology for information retrieval. Journal of Computing and Information Science in Engineering 8(1), 011003.Google Scholar
Liu, Y., Lim, S.C.J., & Lee, W.B. (2013). Product family design through ontology-based faceted component analysis, selection, and optimization. Journal of Mechanical Design 135(8), 081007.CrossRefGoogle Scholar
Lu, W.L., Qin, Y.C., Liu, X.J., Huang, M.F., Zhou, L.P., & Jiang, X.Q. (2015). Enriching the semantics of variational geometric constraint data with ontology. Computer-Aided Design 63, 72–85.Google Scholar
Ming, Z., Yan, Y., Wang, G., Panchal, J.H., Goh, C.H., Allen, J.K., & Mistree, F. (2015). Ontology-based executable design decision template representation and reuse. Proc. ASME Computers and Information in Engineering Conf., Paper No. DETC2015-46272, Boston, August 2–5.Google Scholar
Mistree, F., Hughes, O.F., & Bras, B.A. (1993). The compromise decision support problem and the adaptive linear programming algorithm. In Structural Optimization: Status and Promise (Kamat, M.P., Ed.), pp. 247–286. Washington, DC: AIAA.Google Scholar
Mistree, F., Smith, W., Bras, B., Allen, J., & Muster, D. (1990). Decision-based design: a contemporary paradigm for ship design. Transactions, Society of Naval Architects and Marine Engineers 98, 565–597.Google Scholar
Muster, D., & Mistree, F. (1988). The decision support problem technique in engineering design. International Journal of Applied Engineering Education 4(1), 23–33.Google Scholar
Pahl, G., Pahl, G., Wallace, K., & Blessing, L.T.M. (2007). Engineering Design: A Systematic Approach. London: Springer.Google Scholar
Panchal, J.H., Fernández, M.G., Paredis, C.J.J., & Mistree, F. (2004). Reusable design processes via modular, executable, decision-centric templates. Proc. AIAA/ISSMO Multidisciplinary Analysis and Optimization Conf., Paper No. AIAA-2-4-4601, Albany, NY.Google Scholar
Reddy, R., Smith, W., Mistree, F., Bras, B., Chen, W., Malhotra, A., Badhrinath, K., Lautenschlager, U., Pakala, R., & Vadde, S. (1996). DSIDES User Manual. Atlanta, GA: Georgia Institute of Technology, Woodruff School of Mechanical Engineering, Systems Realization Laboratory.Google Scholar
Resende, C.B., Heckmann, C.G., & Michalek, J.J. (2012). Robust design for profit maximization with aversion to downside risk from parametric uncertainty in consumer choice models. Journal of Mechanical Design 134(10), 100901-1–100901-12.CrossRefGoogle Scholar
Rockwell, J., Grosse, I.R., Krishnamurty, S., & Wileden, J.C. (2009). A decision support ontology for collaborative decision making in engineering design. Proc. Int Symp. Collaborative Technologies and Systems, Baltimore, MD, May 18–22.Google Scholar
Rockwell, J.A., Witherell, P., Fernandes, R., Grosse, I.R., Krishnamurty, S., & Wileden, J.C. (2008). A Web-based environment for documentation and sharing of engineering design knowledge. Proc. 28th Computers and Information in Engineering Conf., Brooklyn, NY, August 3–6.Google Scholar
Sandgren, E. (1990). Nonlinear integer and discrete programming in mechanical design optimization. Journal of Mechanical Design 112(2), 223–229.CrossRefGoogle Scholar
Sandia National Laboratories. (n.d.). Jess@, the Rule Engine for the Java™ Platform. Accessed at http://herzberg.ca.sandia.gov/ on July 15, 2015.Google Scholar
Seepersad, C.C., Allen, J.K., McDowell, D.L., & Mistree, F. (2008). Multifunctional topology design of cellular material structures. Journal of Mechanical Design 130(3), 031404.Google Scholar
Shukla, R., Kulkarni, N., Gautham, B., Singh, A., Mistree, F., Allen, J., & Panchal, J.H. (2015). Design exploration of engineered materials, products, and associated manufacturing processes. Journal of the Minerals, Metals & Materials Society 67(1), 94–107.Google Scholar
Simon, H.A. (1976). Administrative Behavior: A Study of Decision-Making Processes in Administrative Organization. New York: Free Press.Google Scholar
Sivaloganathan, S., & Shahin, T. (1999). Design reuse: an overview. Proc. Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 213(7), 641–654.Google Scholar
Stanford University. (2013). Protégé 3.5 Release. Accessed at http://protegewiki.stanford.edu/wiki/Protege_3.5_Release_Notes on July 15, 2015.Google Scholar
Thurston, D.L. (1991). A formal method for subjective design evaluation with multiple attributes. Research in Engineering Design 3(2), 105–122.Google Scholar
Vadde, S., Allen, J.K., & Mistree, F. (1994). The Bayesian compromise decision-support problem for multilevel design involving uncertainty. Journal of Mechanical Design 116(2), 388–395.CrossRefGoogle Scholar
Wang, H., Noy, N., Rector, A., Musen, M., Redmond, T., Rubin, D., Tu, S., Tudorache, T., Drummond, N., & Horridge, M. (2006). Frames and OWL Side by Side, p. 54. Available at http://protégé.stanford.edu/conference/2006/submissions/slides/7.2wng_protege2006.pdf Google Scholar
Wassenaar, H.J., Chen, W., Cheng, J., & Sudjianto, A. (2005). Enhancing discrete choice demand modeling for decision-based design. Journal of Mechanical Design 127(4), 514–523.Google Scholar
Williams, C.B., Allen, J.K., Rosen, D.W., & Mistree, F. (2007). Designing platforms for customizable products and processes in markets of non-uniform demand. Concurrent Engineering—Research and Applications 15(2), 201–216.CrossRefGoogle Scholar
Witherell, P., Krishnamurty, S., & Grosse, I.R. (2007). Ontologies for supporting engineering design optimization. Journal of Computing and Information Science in Engineering 7(2), 141–150.Google Scholar
Yang, D., Dong, M., & Miao, R. (2008). Development of a product configuration system with an ontology-based approach. Computer-Aided Design 40(8), 863–878.CrossRefGoogle Scholar