Abstract
Abstract A neural network is applied to a optimization problem of material compositions for a hollow circular cylinder of functionally graded material. Using the analytical procedure of a laminated cylinder model, the analytical temperature solution for the cylinder is derived approximately. The thermal stress components are formulated making use of the Airy’s stress function method. As the optimization problem of minimizing the thermal stress distribution, the numerical calculations are carried out making use of neural network, and the optimum material composition is determined under arbitrary temperature range and temperature rise. In addition, the results obtained by neural network and ordinary nonlinear programming method are compared.
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