Concept optimization for mechanical product using Genetic Algorithm |
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Authors: | Hong-Zhong Huang Rui-Feng Bo Xiang-Feng Fan |
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Affiliation: | (1) School of Mechatronics Engineering, University of Electronic Science and Technology of China, 610054 Chengdu, Sichuan, P.R. China;(2) School of Mechanical Engineering, Dalian University of Technology, 116023 Dalian, Liaoning, P.R. China;(3) Department of Mechanical Engineering, University of Albert, T6G 2G8 Edmonton, Albert, Canada |
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Abstract: | Conceptual design is the first step in the overall process of product design. Its intrinsic uncertainty, imprecision, and
lack of information lead to the fact that current conceptual design activities in engineering have not been computerized and
very few CAD systems are available to support conceptual design. In most of the current intelligent design systems, approach
of principle synthesis, such as morphology matrix, bond graphic, or design catalogues, is usually adopted to deal with the
concept generation, in which optional concepts are generally combined and enumerated through function analysis. However, as
a large number of concepts are generated, it is difficult to evaluate and optimize these design candidates using regular algorithm.
It is necessary to develop a new approach or a tool to solve the concept generation. Generally speaking, concept generation
is a problem of concept synthesis. In substance, this process of developing design candidate is a combinatorial optimization
process, viz., the process of concept generation can be regarded as a solution for a state-place composed of multi-concepts.
In this paper, genetic algorithm is utilized as a feasible tool to solve the problem of combinatorial optimizalion in concept
generation, in which the encoding method of morphology matrix based on function analysis is applied, and a sequence of optimal
concepts are generated through the search and iterative process which is controlled by genetic operators, including selection,
crossover, mutation, and reproduction in GA. Several crucial problems on GA are discussed in this paper, such as the calculation
of fitness value and the criteria for heredity termination, which have a heavy effect on selection of better concepts. The
feasibility and intellectualization of the proposed approach are demonstrated with an engineering case. In this work concept
generation is implemented using GA, which can facilitate not only generating several better concepts, but also selecting the
best concept. Thus optimal concepts can be conveniently developed and design efficiency can be greatly improved. |
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Keywords: | Conceptual Design Genetic Algorithm Optimization Concept Generation Intelligence |
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