|
Published Articles >> Table of Contents >> Abstract
June 1992 (Vol. 7, No. 3)
pp. 52-59
Creating and Using Models for Engineering Design: A Machine-Learning Approach
Sudhakar Yerramareddy
David K. Tcheng
Stephen C-Y. Lu
Dennis N. Assanis
Full Article Text:
  
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/64.143239
Send link to a friend
| Abstract |
|
An adaptive and interactive modeling system (AIMS) that integrates simulation, optimization and machine learning to help engineers make design decisions is described. AIMS views engineering decision making as a two-phase process of creating and then using models. The competitive relation learner and the induce-and-select optimizer, AIMS's two main components, and their roles in both phases of decision-making are discussed. AIMS's role in supporting the design of a diesel engine that outputs power within the 440- to 460-kW range and consumes the least amount of fuel is also discussed.
|
 References
|
[1] K.J. Bathe,Finite-Element Procedures in Engineering Analysis, Prentice-Hall, Englewood Cliffs, N.J., 1982.
[2] G.M. Zhang et al., "Simulation of Surface Topography Formed During the Intermittent Turning Process,"Proc. ASME Winter Ann. Meeting, Vol. DSC 18, ASME, New York, 1989, pp. 11-18.
[3] D.N. Assanis and J.B. Heywood, "Development and Use of a Computer Simulation of the Turbocompounded Diesel System for Engine Performance and Component Heat Transfer Studies,"The Adiabatic Engine: Global Developments, Soc. of Automotive Engineers, Warrendale, Pa., 1986, pp. 95-120.
[4] G.N. Vanderplaats,Numerical Optimization Techniques for Engineering Design: With Applications, McGraw-Hill, New York, 1984.
[5] S.C-Y. Lu, "Machine-Learning Techniques for Group Technology Applications,"CIRP Annals(International Institution for Production Engineering Research), Vol. 38, No. 1, 1989, pp. 455-459.
[6] S.C-Y. Lu and K. Chen, "A Machine-Learning Approach to the Automatic Synthesis of Mechanistic Knowledge for Engineering Decision Making,"J. Artificial Intelligence for Eng. Design, Analysis, and Manufacturing, Vol. 1, No. 2, 1987, pp. 109-118.
[7] D.A. Tortorelli, R.B. Haber, and S.C-Y. Lu, "Shape Sensitivities for Nonlinear Dynamic Thermoelastic Systems,"Proc. AIAA/ ASME/ASCE/AHS/ASC 30th Structures, Structural Mechanics, and Materials Conf., American Institute of Aeronautics and Astronautics, Washington, D.C., 1989, pp. 1,321-1,329.
[8] L. Rendell, "A New Basis for State-Space Learning Systems and a Successful Implementation,"Artificial Intelligence, Vol. 20, No. 4, 1983, pp. 369-392.
[9] J. R. Quinlan, "Induction of decision trees,"Machine Learning, vol. 1, no. 1, pp. 81-106, 1986.
[10] L. Breiman et al.,Classification and Regression Trees, Wadsworth Int'l Group, Belmont, Calif., 1984.
[11] D. Tcheng, "Building Robust Learning Systems by Combining Induction and Optimization,"Proc. 11th Int'l Joint Conf. on Artificial Intelligence(IJCAI 89), Morgan Kaufmann, San Mateo, Calif., 1989.
[12] Parallel Distributed Processing, D. Rumelhart and J. McClelland, eds., MIT Press, Cambridge, Mass., 1986.
|
 Additional Information
|
Citation:
Sudhakar Yerramareddy, David K. Tcheng, Stephen C-Y. Lu, Dennis N. Assanis,
"Creating and Using Models for Engineering Design: A Machine-Learning Approach,"
IEEE Expert: Intelligent Systems and Their Applications,
vol. 07,
no. 3,
pp. 52-59,
Jun.,
1992
|
|
RSS Feed