Abstract
Manufacturing the complex surface plates in stern and stem is a major factor in cost of a preliminary ship design by computing process. If these hull plate parts are effectively classified, it helps to compute the processing cost and find the way of cut-down the processing cost. This paper presents a new method to classify surface plates effectively in preliminary ship design using neural network. A neural network based ship hull plate classification programme developed and tested for the automatic classification of ship design. The input variables are regarded as Gaussian curvature distributions on the plate. Various applicable rules of network topology are applied in ship design. By observing the results of the proposed method, the effectiveness of the proposed method are discussed. As a result, high prediction rate was achieved in ship design. And the proposed method will contribute to reduce the production cost of ship.
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