Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-25T11:24:24.076Z Has data issue: false hasContentIssue false
  • English
  • Français

A model of the mechanical design process based on empirical data

Published online by Cambridge University Press:  27 February 2009

David G. Ullman ,
Thomas G. Dietterich  and
Larry A. Stauffer
David G. Ullman
Affiliation:
Department of Mechanical Engineering, Oregon State University, U.S.A.
Thomas G. Dietterich
Affiliation:
Department of Computer Science, Oregon State University, U.S.A.
Larry A. Stauffer
Affiliation:
Department of Mechanical Engineering, University of Idaho, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper describes the task/episode accumulation model (TEA model) of non-routine mechanical design, which was developed after detailed analysis of the audio and video protocols of five mechanical designers. The model is able to explain the behavior of designers at a much finer level of detail than previous models. The key features of the model are (a) the design is constructed by incrementally refining and patching an initial conceptual design, (b) design alternatives are not considered outside the boundaries of design episodes (which are short stretches of problem solving aimed at specific goals), (c) the design process is controlled locally, primarily at the level of individual episodes. Among the implications of the model are the following: (a) CAD tools should be extended to represent the state of the design at more abstract levels, (b) CAD tools should help the designer manage constraints, and (c) CAD tools should be designed to give cognitive support to the designer.

Type
Research Article
Information
AI EDAM , Volume 2 , Issue 1 , February 1988 , pp. 33 - 52
Copyright
Copyright © Cambridge University Press 1988

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

Adelson, B. 1985. Comparing natural and abstract categories: a case study from computer science. Cognitive Science 9, 417–430.CrossRefGoogle Scholar
Adelson, B. and Soloway, E. 1984. A cognitive model of software design. Rep. No. 342, Department of Computer Science, Yale University.Google Scholar
Akin, O. 1986. Psychology of Architectural Design. England: Pion.Google Scholar
Fickas, S. 1985. Automating the transformational development of software. IEEE Transactions on Software Engineering SE-11 (11), 1268–1277.CrossRefGoogle Scholar
Hayes-Roth, B. 1985. A blackboard architecture for control. Artificial Intelligence 26 251–322.CrossRefGoogle Scholar
Kant, E. 1985 Understanding and automating algorithm design. In Proceedings of IJCAI-85. Los Altos, CA: Morgan-Kaufmann, pp. 1243–1253.Google Scholar
Kant, E. and Newell, A. 1982. Problem-solving techniques for the design of algorithms. Rep No. CMU-CS-82–145. Department of Computer Science, Carnegie-Mellon University.CrossRefGoogle Scholar
Laird, J. E., Newell, A., and Rosenbloom, P. S. 1987. SOAR: An architecture for general intelligence. Artificial Intelligence 33, 1–64.CrossRefGoogle Scholar
Marcus, S., Stout, J. and McDermott, J. 1987. VT. an expert elevator designer. AI Magazine 8(4), 39–56.Google Scholar
Miller, G. A. 1956. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review 63, 81–97.CrossRefGoogle ScholarPubMed
Newell, A. and Simon, H. A. 1972 Human Problem Problem Solving. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Sacerdoti, E. D. 1974. Planning in a hierarchy of abstraction spaces. Artificial Intelligence 5(2), 11–135.CrossRefGoogle Scholar
Stauffer, L. 1987. An empirical study on the process of mechanical design. Thesis, Department of Mechanical Engineering, Oregon State University, Corvallis, OR.Google Scholar
Stauffer, L. and Ullman, D. G. 1987. A comparison of the results of empirical studies into the mechanical design process. Submitted to: Design Studies.CrossRefGoogle Scholar
Stauffer, L, Ullman, D. G. and Dietterich, T. G. 1987. Protocol analysis of mechanical engineering design. Proceedings 1987 International Conference on Engineering Design, WDK 13, 68–73.Google Scholar
Steinberg, Louis I. 1987. Design as refinement plus constraint propagation: the VEXED experience. Proceedings Sixth National Conference on Artificial Intelligence (AAAI-87). Los Altos, CA: Morgan-Kaufmann, pp. 830–835.Google Scholar
Sussman, G. J. and Steele, G. L. Jr, 1980. CONSTRAINTS—a language for expressing almost hierarchical descriptions. Artificial Intelligence 14, 1–39.CrossRefGoogle Scholar
Tikerpuu, J. and Ullman, D. G. Data representations for mechanical design based on empirical data. Submitted to 1988 International Computers in Engineering Conference.Google Scholar
Ullman, D. G. and Dietterich, T. G. 1987 Mechanical design methodology: implications on future developments of computer- aided design and knowledge-based systems Engineering with Computers, No 2, 21–29.CrossRefGoogle Scholar
Ullman, D. G., Stauffer, L., and Dietterich, T. G 1987 a Preliminary results on an experimental study of mechanical design Proceedings NSF Workshop on Design Theory and Methodology, pp. 145–188. Available as Report Number 86–30–9 Department of Computer Science, Oregon State University.Google Scholar
Ullman, D. G., Stauffer, L., and Dietterich, T. G. 1987 b. Toward expert CAD. ASME, Computers in Mechanical Engineering 6(3), 21–29.Google Scholar
Ulrich, K. T. and Seering, W. P. 1988. Function sharing in mechanical design. Proceedings of AAAI-88.Google Scholar