Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-23T11:00:56.512Z Has data issue: false hasContentIssue false
  • English
  • Français

On the evolution of CAE research1

Published online by Cambridge University Press:  27 February 2009

Clive L. Dym  and
Raymond E. Levitt
Clive L. Dym
Affiliation:
Department of Engineering, Harvey Mudd College, Claremont, CA91711–5990.
Raymond E. Levitt
Affiliation:
Department of Civil Engineering, Stanford University, Stanford, CA94305.
Get access Rights & Permissions [Opens in a new window]

Abstract

Less than a decade ago it seemed that a new paradigm of engineering–called computer-aided engineering (CAE) – was emerging. This emergence was driven in part by the success of computer support for the tasks of engineering analysis and in part by a new understanding of how computational ideas largely rooted in artificial intelligence (AI) could perhaps improve the practice of engineering, especially in the area of design synthesis. However, while this “revolution” has failed to take root or flourish as a separate discipline, it has spawned research that is very different from traditional engineering research. To the extent that such CAE research is different in style and paradigm, it must also be evaluated according to different metrics. Some of the metrics that can be used are suggested, and some of the evaluation issues that remain as open questions are pointed out.

Keywords

CAE ResearchComputational ParadigmsDesigners’ Assistants
Type
Articles
Information
AI EDAM , Volume 8 , Issue 4 , Fall 1994 , pp. 275 - 282
Copyright
Copyright © Cambridge University Press 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Darwiche, A., Levitt, R.E., & Hayes-Roth, B. (1989). OARPLAN: Generating project plans by reasoning about objects, actions and resources. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 2(3), 169–181.CrossRefGoogle Scholar
Demel, C.T., Dym, C.L., Summers, M.D., & Wong, C.S. (1992). DEEP: A Knowledge-Based (Expert) System for Electrical Plat Design. Technical Report, Southern California Edison — Harvey Mudd College Center for Excellence in Electrical Systems, December.Google Scholar
Dym, C.L. (1993). The role of symbolic representation in engineering design education. IEEE Trans. Education 36(1), 187–193.CrossRefGoogle Scholar
Dym, C.L. (1994). Engineering Design: A Synthesis of Views. Cambridge University Press, New York.Google Scholar
Dym, C.L., Henchey, R.P., Delis, E.A., & Gonick, S. (1988). A knowledge-based system for automated architectural code-checking. Computer-Aided Design 20(3), 137–145.Google Scholar
Dym, C.L., & Levitt, R.E. (1991 a). Knowledge-Based Systems in Engineering. McGraw-Hill, New York.Google Scholar
Dym, C.L., & Levitt, R.E. (1991 b). Toward the integration of knowledge for engineering modeling and computation. Engineering with Computers 7(4), 209–224.CrossRefGoogle Scholar
Dym, C.L., & Levitt, R.E. (1994). Whatever happened to CAE? In Bridging the Generations: Proc. Int. Workshop on the Future Directions of Computer-Aided Engineering, (Rehak, D.R., Ed.). Carnegie Mellon University, Pittsburgh, Pennsylvania.Google Scholar
Feigenbaum, E.A. (1993). Tiger in a cage: The applications of knowledge-based systems. Keynote Lecture, AAAl Symposium, Washington, DC.Google Scholar
Fenves, S.J. (1993). Personal communication, June.Google Scholar
Levitt, R.E. (1990). Merging artificial intelligence with CAD: Intelligent, model-based engineering. Henry M. Shaw Memorial Lecture, North Carolina State University, Raleigh, North Carolina; reprinted as Working Paper No. 28, Center for Integrated Facility Engineering, Stanford University, Stanford, California.Google Scholar
Levitt, R.E., Cohen, G.P., Kunz, J.C., Nass, C.I., Christiansen, T., & Jin, Y. (1994). “The Virtual Design Team”: Simulating how organization structure and information processing tools affect team performance. In Computational Organization Theory, (Carly, K. & Prietula, M., Eds.). Lawrence Erlbaum Associates, Hillsdale, New Jersey.Google Scholar
Mishelevich, D.J., Katajamaki, M., Karras, T., Lehtimaki, H., Axworthy, A., Riitahuhta, A., & Levitt, R.E. (1991). An open-architecture approach to knowledge-based CAD. In Artificial Intelligence in Engineering Design, (Tong, C. & Sriram, D., Eds.), Vol. III, Academic Press, New York.Google Scholar
Mittal, S., Dym, C.L., & Morjaria, M. (1986). PRIDE: An expert system for the design of paper handling systems. Computer 19(7), 102–114.Google Scholar
Oralkan, G.A., Staudinger, J., Levitt, R.E., & Roberts, P.V. (1994). The HAZTIMATOR: Knowledge-based time and cost estimating of hazardous waste remediation projects. Working Paper No. 23, Center for Integrated Facility Engineering, Stanford University, Stanford, California.Google Scholar
Reich, Y. (1994). What is wrong with CAE and can it be fixed? In Bridging the Generations: Proc. Int. Workshop on the Future Directions of Computer-Aided Engineering, (Rehak, D.R., Ed.). Carnegie Mellon University, Pittsburgh, Pennsylvania.Google Scholar
Riitahuhta, A. (1988). Systematic engineering design and use of an expert system in boiler plant design. Proc. ICED Int. Conf. Engineering Design, Budapest, Hungary.Google Scholar
Texedeau, J., & Souchet, R. (1991). Innovative architecture realized using the finite element method. Structural Engineering Rev. 3(3), 181–185.Google Scholar