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Factors that affect planning in a knowledge-based system for mechanical engineering design optimization with application to the design of mechanical power transmissions

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Abstract

Intelligent computer-aided design (CAD) emulates the human activity of design so that production planning, decision making, and inventive design can be performed by computers.

Based on the history of human experience in engineering design, a formalized and systematic approach to design should include procedures from (1) conceptual design, (2) layout design, and (3) numerical optimization. The highest level within such a system should be responsible for specifying and symbolically optimizing skeleton structures of generic (nonspecific) elements within the design process that are eventually to be specified uniquely (pinned down) and ultimately optimized numerically. Planning plays a key role in such a system.

Planning has been utilized as a tool for process organization within the knowledge domains of chemical engineering, electrical engineering, manufacturing, as well as for general problem formulation and solution. State estimation, subtask scheduling, and constraint propagation have been found to be factors of prime importance in this type of problem.

Problems associated with the implementation of a planning strategy within a knowledge-based system for mechanical engineering design optimization are discussed. A hypothesis for planning is put forth and examined within the context of a model of the mechanical design optimization process. An example that demonstrates the applicability of this approach to mechanical power transmission design is considered.

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References

  1. Pahl, G.; Beitz, W. (1984) Engineering Design, The Design Council (Ed. K. Wallace), London: Springer-Verlag

    Google Scholar 

  2. Chieng, W.H.; Hoeltzel, D.A. (1987) An interactive hybrid (symbolic-numeric) system approach to near optimal design of mechanical components. Eng. Comput. 2, 111–123

    Google Scholar 

  3. Kitzmiller, C.T.; Kowalik, J.S. (1987) Coupling symbolic and numeric computing in KB systems. AI Magazine 8(2), 85–90

    Google Scholar 

  4. Stefik, M. (1981) Planning and meta-planning (MOLGEN: parts 1 and 2). 16, 111–139

  5. Gongaware, T.; Hsieh, L.; McGuire, P.; Volgel, S. (1983) Application of automated design process planning to electronics manufacturing. In: Proceedings of CAMI's, 12th Annual Meeting and Technical Conference.

  6. Descotte, Y.; Latombe, J.C. (1985) Making compromises among antagonist constraints in a planner, AI 27, 183–217

    Google Scholar 

  7. Sacerdoti, E. (1977) A Structure for Plans and Behavior. Amsterdam: North-Holland

    Google Scholar 

  8. Wilkins, D.E. (1984) Domain-independent planning: Representation and plan generation. AI 22, 269–301

    Google Scholar 

  9. Azarm, S.; Li, W-C. (1987) Optimal design using a two-level monotonicity-based decomposition method. ASME Pub. DE-Vol. 10-1, 41–48

    Google Scholar 

  10. Azarm, S.; Li, W-C. (1987) Optimum design using decomposition methods. Invited paper in the Proceedings of the NSF Workshop: The Study of the Design Process. Oakland, CA, February 8–10, pp. 453–502

  11. Yoshikawa, H. (1980) General design theory and a CAD system. Man-Machine Communication in CAD/CAM (Eds. T. Sata; E.A. Warman), pp. 35–58. Amsterdam, North-Holland

    Google Scholar 

  12. Israel, D.J. (1983) The role of logic in knowledge representation, IEEE Comput. 16, 37–41

    Google Scholar 

  13. Choy, J.K., Agogino, A.M. (1986) SYMON: Automated symbolic monotonicity analysis system for qualitative design optimization. AMSE International Computer in Engineering Conference, Chicago, pp. 207–212

  14. Charniak, E.; McDermott, D. (1985) Introduction to Artificial Intelligence. Reading, MA: Addison-Wesley

    Google Scholar 

  15. Lebowitz, M. (1986) Concept learning in a rich input domain: Generalization-based memory. In: Machine Learning: An Artificial Intelligence Approach (Ed. R.S. Michalski, J.G. Carbonell, T.M. Mitchell), Vol. II, pp. 193–214, Los Altos, CA: Morgan Kaufmann

    Google Scholar 

  16. Hayes, R.F.; Waterman, D.A. (1986) Building Expert Systems. Reading, MA: Addison-Wesley, pp. 89–106

    Google Scholar 

  17. Winston, P.H. (1980) Learning and reasoning by analogy. Commun. ACM 23(12), 689–702

    Google Scholar 

  18. Dejong, G. (1986) An approach to learning from observations. In: Machine Learning: An Artificial Intelligence Applroach (Ed. R.S. Michalski, J.G. Carbonell, T.M. Mitchell), Vol. II, pp. 571–590. Los Atlos, CA: Morgan Kaufmann

    Google Scholar 

  19. Vanderplatts, G.N. (1985) COPES/ADS a Fortran control program for engineering synthesis using the ADS optimization program. Santa Barbara: Engineering Design Optimization (EDO) Inc., June

    Google Scholar 

  20. Hoeltzel, D.A.; Chieng, W.H. (1987) Statistical mechanine learning for the cognitive selection of nonlinear programming algorithms in engineering design optimization. Transaction of Fifth Army Conference on Applied Math & Computing, ARO Report 88-1, pp. 65–78

    Google Scholar 

  21. Quinlan, J.R. (1982) Semi-autonomous acquisition of pattern-based knowledge. Introductory Reading in Expert System (Ed. D. Mitchie), pp. 192–207. New York: Gordon and Breach Pub.

    Google Scholar 

  22. Dixon, J.R.; Howe, A.; Cohen, P.R.; Simmons, M.K. (1986) DOMINIC: Progress toward independence in design by iterative redesign. IN: Proceedings of ASMA International Computer in Engineering Conference, Chicago, pp. 199–206

  23. Zedah, L.A. (1983) Commonsense knowledge representation based on fuzzy logic. IEEE Comput. 16 (October), 61–65

    Google Scholar 

  24. Ullman, D.G.; Dietterich, T.A. (1986) Mechanical design methodology: Implementation on future developments of computer-aided design and knowledge-based systems. In: Proceedings of ASME International Computers in Engineering Conference, Chicago, pp. 173–180

  25. Love, S.F. (1984) Planning and Creating Successful Engineering Designs. New York: Van Nostrand Reinhold

    Google Scholar 

  26. Nilsson, N. (1971) Problem Solving Methods in Artificial Intelligence. New York: McGraw-Hill

    Google Scholar 

  27. Buchanan, B.G.; Shortliffe, E.H. (1984) Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Reading, MA: Addison-Wesley

    Google Scholar 

  28. Middendorf, W.H. (1986) Design of Devices and Systems. New York: Marcel Dekker

    Google Scholar 

  29. Brown, D.C.; Chandrasekaran, B. (1986) Knowledge and control for a mechanical design expert system. IEEE Comput 19 (July), 92–100

    Google Scholar 

  30. Winston, P.H. (1982) Learning new principles from precedents and exercises. AI 19, 321–350

    Google Scholar 

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Authors and Affiliations

  1. Laboratory for Intelligent Design, Department of Mechanical Engineering, Columbia University, 10027, New York, NY, USA

    David A. Hoeltzel & Wei-Hua Chieng

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  1. David A. Hoeltzel
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  2. Wei-Hua Chieng
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Hoeltzel, D.A., Chieng, WH. Factors that affect planning in a knowledge-based system for mechanical engineering design optimization with application to the design of mechanical power transmissions. Engineering with Computers 5, 47–62 (1989). https://doi.org/10.1007/BF01201997

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