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Abstract
Introduction
Related Work
Conclusions and Future Work
References
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Engineering
Industrial Engineering

Ontologies for supporting engineering analysis models

Profile image of Ian GrosseIan Grosse

2005, AI EDAM

Abstract

In this paper we lay the foundations for exchanging, adapting, and interoperating engineering analysis models (EAMs). Our primary foundation is based upon the concept that engineering analysis models are knowledge-based abstractions of physical systems, and therefore knowledge sharing is the key to exchanging, adapting, and interoperating EAMs within or across organizations. To enable robust knowledge sharing, we propose a formal set of ontologies for classifying analysis modeling knowledge. To this end, the fundamental concepts that form the basis of all engineering analysis models are identified, described, and typed for implementation into a computational environment. This generic engineering analysis modeling ontology is extended to include distinct analysis subclasses. We discuss extension of the generic engineering analysis modeling class for two common analysis subclasses: continuum-based finite element models and lumped parameter or discrete analysis models. To illustrate ho...

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The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately.

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References (53)

  1. Alberts, L.K., & Dikker, F. ~1992!. Integrating standards and synthesis knowledge using the YMIR ontology. In Artificial Intelligence in Design ~Gero, J.S., & Sudweeks, F., Eds.!, pp. 517-534. Boston: Kluwer Academic. Bachant, J. ~1988!. RIME: preliminary work toward a knowledge acquisi- tion tool. In Automatic Knowledge for Acquisition for Expert System ~Marcus, S., Ed.!, pp. 201-224. Boston: Kluwer Academic.
  2. Batory, D., Singhal, V., Thomas, J., Dasari, S., & Sirkin, M. ~1994!. The GenVoca model of software-system generation. IEEE Software 11(5), 89-94.
  3. Bennett, J., Cleary, L., Englemore, R., & Melosh, R. ~1978!. SACON: A Knowledge-Based Consultant for Structural Analysis. Report No. STAN-CS-699. Stanford, CA: Stanford University, Department of Com- puter Science.
  4. Borst, P., Pos, A., Top, J.L., & Akkermans, J.M. ~1994!. Physical systems ontology. In Working Papers of the European Conf. Artificial Intelli- gence ECAI'94 Workshop on Implemented Ontologies ~Mars, N.J.I., Ed.!, pp. 47-80. Amsterdam: ECCAI.
  5. Borst, P., Akkermans, J.M., Pos, A., & Top, J.L. ~1995!. The PhysSys ontology for physical systems. In Working Papers of the Ninth Int. Workshop on Qualitative Reasoning QR'95 ~Bredeweg, B., Ed.!, pp. 11-21. University of Amsterdam.
  6. Brunnermeier, S.B., & Martin, S.A. ~1999!. Interoperability Cost Analysis of the US Automotive Supply Chain, Final Technical Report To NIST. RTI Project No. 7007-03. Research Triangle Park, NC: Research Tri- angle Institute.
  7. Buchanan, B.G., & Shortliffe, E.H. ~1984!. Uncertainty and evidential support. In Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristics Programming Project. Boston: Addison-Wesley.
  8. Chandrasekaran, B., Goel, A., & Iwasaki, Y. ~1993!. Functional represen- tation as design rationale. IEEE Computer January, 48-56.
  9. Clancey, W.J. ~1983!. The epistemology of rule-based expert system: a framework for explanation. Artificial Intelligence 20, 215-251.
  10. Clancey, W.J. ~1985!. Heuristic classification. Artificial Intelligence 27(3), December.
  11. de Kleer, J., & Brown, J.S. ~1983!. Assumptions and ambiguities in mech- anistic mental models. In Mental models ~Genter, D., & Stevens, E.L., Eds.!, pp. 155-190. Hillsdale, NJ: Erlbaum.
  12. Doraiswamy, S., Krishnamurty, S., & Grosse, I. ~1999!. Decision making in finite element analysis. Proc. 1999 Design Technical Conf., DETC990 CIE-9058, September. Las Vegas, NV: ASME.
  13. Dubois-Perlerin, Y., & Zimmermann, T. ~1993!. Object-oriented finite element programming: III. An efficient implementation in Cϩϩ. Com- puter Methods in Applied Mechanics and Engineering, 108, 165-183.
  14. Duda, R.O., Gasching, J., Hart, E., Konolige, K., Reboh, R., Barrett, P., & Slocum, J. ~1978!. Development of the PROSPECTOR consultation system for mineral exploration, final report. In SRI Projects 5821 and 6415. Menlo Park, CA: SRI International.
  15. Dym, C., & Levitt, R. ~1991!. Toward the integration of knowledge for engineering modeling and computation. Engineering with Computers 7, 209-224.
  16. Finn, D., & Cunningham, P. ~1994!. Physical model generation in PDE analysis using model-based case-based reasoning. QR '94: 8th Int. Workshop on Qualitative Reasoning About Physical Systems, pp. 90-97, Nara, Japan, June.
  17. Finn, D., Grimson, J.B., & Harty, N.M. ~1992!. An intelligent mathemat- ical modeling assistant for analysis of physical systems. Proc. ASME 1992 Computers in Engineering Conf. Exposition, Vol. 2, pp. 69-74. San Diego, CA: ASME.
  18. Goel, A., Bhatta, S., & Stroulia, E. ~1996a!. KRITIK: an early case-based design system. In Issues and Applications of Case-Based Reasoning to Design ~Maher, M., & Pu, P., Eds.!. Mahwah, NJ: Erlbaum.
  19. Goel, A., Gomez, A., Grue, N., Murdock, J.W., Recker, M., & Govindaraj, T. ~1996b!. Explanatory interface in interactive design environments. In Artificial Intelligence in Design ~Gero, J.S., Ed.!. Boston: Kluwer Academic.
  20. Grosso, W.E., Eriksson, H., Fergerson, R.W., Gennari, J.H., Tu, S.W., & Musen, M.A. ~1999!. Knowledge Modeling at the Millennium (The Design and Evolution of Protégé-2000). Stanford's Medical Informat- ics Report No. SMI-1999-0801. Stanford, CA: Stanford University.
  21. Grower, M.D. ~1982!. A Pragmatic Knowledge Acquisition Methodology. Redondo Beach, CA: TRW Defense Systems Group.
  22. Gruber, T.R. ~1993!. A translation approach to portable ontologies. Knowl- edge Acquisition 5(2), 199-220.
  23. Gruber, T., & Olsen, G. ~1994!. An ontology for engineering mathematics. Proc. Fourth Int. Conf. Principles of Knowledge Representation and Reasoning ~Doyle, J., Torasso, P., & Sandewall, E., Eds.!, pp. 258- 269. San Mateo, CA: Morgan-Kaufmann.
  24. Hazelrigg, G.A. ~1996!. Systems Engineering: An Approach to Information- Based Design. New York: Prentice-Hall.
  25. Henson, B., Juster, N., & de Pennington, A. ~1994!. Towards an integrated representation of function, behavior and form, computer aided concep- tual design. Proc. 1994 Lancaster Int. Workshop on Engineering Design ~Sharpe, J., & Oh, V., Eds.!, pp. 95-111. Lancaster: Lancaster Univer- sity EDC.
  26. Holzhauer, D., & Grosse, I. ~1999!. Finite element analysis using compo- nent decomposition and knowledge-based control. Engineering with Computers 15, 315-325.
  27. ISO 10303-104. ~1994!. Industrial Automation Systems and Integrations- Product Data Representation and Exchange-Part 104: Integrated Application Resource: Finite Element Analysis. ISO TC184, SC4. New York: ISO, ISO Technical Committee 184, Subcommittee 4.
  28. Iwasaki, Y., & Chandrasekaran, B. ~1992!. Design verification through function and behavior-oriented representations: bridging the gap between function and behavior. In Artificial Intelligence in Design ~Gero, J.S., Ed.!, pp. 597-616. Boston: Kluwer Academic.
  29. Java Native Interface Specification. ~1997!. Cupertino, CA: Sun Micro- systems, Inc.
  30. Mackie, R.I. ~1992!. Object oriented programming of the finite element method. International Journal for Numerical Methods in Engineering 35, 425-436.
  31. McDermott, J. ~1980!. R1: an expert in the computer system domain. In Proc. National Conf. Artificial Intelligence, AAAI, pp 269-271.
  32. Miller, R.A., Pople, H.E., & Myers, J.D. ~1982!. INTERNIST-I: an exper- imental computer based diagnostic consultant for general internal med- icine. New England Journal of Medicine 306(8), 468-476.
  33. Musen, M.A. ~2000!. Ontology-oriented design and programming. In Knowledge Engineering and Agent Technology ~Cuena, J., Demazeau, Y., Garcia, A, & Treur, J., Eds.!. Amsterdam: IOS Press.
  34. Noy, F. N., & McGuinness, D.L. ~2001!. Ontology Development 101: A Guide to Creating Your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Infor- matics Technical Report SMI-2001-0880. Stanford, CA: Stanford University.
  35. Paredis, C.J.J., Diaz-Calderon, A., Sinha, R., & Khosla, P.K. ~2000!. Com- posable models for simulation-based design. Engineering with Com- puters 17, 112-128.
  36. Peak, R.S. ~2000!. X-Analysis Integration Technology. Technical Report EL002-2000A. Atlanta, GA: Georgia Institute of Technology, Engi- neering Information Systems Lab.
  37. Qian, L., & Gero, J.S. ~1996!. Function-behavior-structure paths and their role in analogy based design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(4), 289-312.
  38. Ranta, M., Mantyla, M., Umeda, Y., & Tomiyama, T. ~1996!. Integration of functional and feature based product modeling-the IMS0GNOSIS Experience. Computer-Aided Design 28(5), 371-381.
  39. Reed, J.A., & Afjeh, A.A. ~1998!. An object-oriented framework for dis- tributed computational simulation of aerospace propulsion systems. Proc. 4th USENIX Conf. Object-Oriented Technologies and Systems (COOTS), Santa Fe, NM, April 27-30.
  40. Rohl, P.J., Kolonay, R.K., Irani, R.M., Sobolewski, M., Kao, K., & Bailey, M.W. ~2000!. A federated intelligent product environment. In AIAA- 2000, pp. 5-6. Paper No. AIAA-2000-4902.
  41. Shanbhag, S. ~2001!. Metaobject based finite element modeling. MS The- sis. Amherst, MA: University of Massachusetts.
  42. Shanbhag, S., Grosse, I.R., Wileden, J.C., & Kaplan, A. ~2001!. Meta-object based finite element analysis. Paper No. DETC20010DAC-21062. Proc. ASME 2001 Design Automation Conf. Pittsburgh, PA: ASME.
  43. Sheehy, M., & Grosse, I. ~1997!. An object-oriented blackboard based approach for automated finite element modeling and analysis of mul- tichip modules. Engineering with Computers 13, 197-210.
  44. Sinha, R., Liang, V.C., Paredis, C.J.J., & Khosla, P.K. ~2001!. Modeling and simulation methods for design of engineering systems. Journal of Computing and Information Science in Engineering 1, 84-91.
  45. Shephard, M.S., Bachmann, L., Georges, M.K., & Korngold, E.V. ~1990!. Framework for reliable generation and control of analysis idealisa- tions. Computer Methods in Applied Mechanics and Engineering 82(1- 3), 257-280.
  46. Szykman, S., Fenves, S.J., Keirouz, W., & Shooter, S.B. ~2000!. A foun- dation for interoperability in next generation product development sys- tems. Proc. 2000 ASME Design Engineering Technical Conf., Baltimore, MD, September, pp. 10-13. New York: ASME.
  47. Tatsubori, M. ~1999!. An extension mechanism for the Java language. MS Thesis. Ibaraki, Japan: University of Tsukuba.
  48. Tomiyama, T., Kiriyama, T., Takeda, H., & Xue, D. ~1989!. Metamodel: a key to intelligent CAD systems. Research in Engineering Design 1, 19-34.
  49. Turkiyyah, G.M., & Fenves, S.J. ~1996!. Knowledge-based assistance for finite element modeling. AI Applications in Civil and Structural Engi- neering 11(3), 23-32.
  50. Umeda, Y., Ishii, M., Yoshioka, M., Shimomura, Y., & Tomiyama, T. ~1996!. Supporting conceptual design based on the function-behavior-state modeler. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, 275-288.
  51. van Melle, W. ~1978!. A Domain Independent System That Aides in Con- structing Consultation Programs. Report HPP-78-19. Stanford, CA: Stanford University, Computer Science Department.
  52. Wujek, B., Koch, P., McMillan, M., & Chiang, W.-S. ~2002!. A distrib- uted, component-based integration environment for multidisciplinary optimal and quality design. 9th AIAA0ISSMO Symp. Multidisciplinary Analysis and Optimization, Atlanta, GA, September.
  53. Zimmermann, T., Dubois-Perlerin, Y., & Bomme, P. ~1992!. Object- oriented finite element programming: I. Governing principles. Com- puter Methods in Applied Mechanics and Engineering 98, 291-303.

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