Evolutionary optimization within an intelligent hybrid system for design integration

Authors:
D. Su;M. Wakelam
Affiliations:
Department of Mechanical and Manufacturing Engineering, The Nottingham Trent University, Burton Street, Nottingham, NG1 4BU, U.K.;Department of Mechanical and Manufacturing Engineering, The Nottingham Trent University, Burton Street, Nottingham, NG1 4BU, U.K.
Venue:
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Year:
1999

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Abstract

An intelligent hybrid approach has been developed to integrate various stages in total design, including formulation of product design specifications, conceptual design, detail design, and manufacture. The integration is achieved by blending multiple artificial intelligence (AI) techniques and CAD/CAE/CAM into a single environment. It has been applied into power transmission system design. In addition to knowledge-based systems and artificial neural networks, another AI technique, genetic algorithms (GAs), are involved in the approach. The GA is used to conduct optimization tasks: (1) searching the best combination of design parameters to obtain optimum design of gears, and (2) optimization of the architecture of the artificial neural networks used in the hybrid system. In this paper, after a brief overview of the intelligent hybrid system, the GA applications are described in detail.