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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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