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The topology optimization under material and clearness constraints is studied in this thesis. The objectives include minimum compliance and maximum fundamental eigenvalue. The multi-objective topology optimization using fuzzy decision making is also explored. The main purpose of this thesis is to find optimum topology in a variable-boundary design space. Taguchi method and genetic algorithm are used to find the optimum boundaries of the design space. Five levels of boundary locations are given in Taguchi. A minimum number of experiments based on orthogonal table are performed to determine the optimum boundary locations. Genetic algorithm is a computational intensive approach. To save computational time, artificial neural network is trained to do the structural analyses. A Unix shell script is developed to accomplish the design optimization loop. The sequential linear programming algorithm is utilized to solve the optimization problem. Six different examples demonstrate applications of the theory. The results of each example by Taguchi method and genetic algorithm are compared. P3/PATRAN is used to show the topologies in optimal design spaces.
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