Automation of Hull Plates Classification in Ship Design System Using Neural Network Method

摘要

Manufacturing the complex surface plates in stern and stem is a major factor in cost of a preliminary ship design by computing process. 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 program was developed and tested for automatic classification of ship design. The input variables are regarded as Gaussian curvature distributions on the plate. In automation of hull plate classification, two different number of input variable were used. By observing the results of the proposed method, the effectiveness of the proposed method are discussed. As a result, 94% prediction rate was achieved in ship design, which is a remarkable advance in design system compared with the conventional method. Accordingly, in the initial design stage, the ship hull plate classification program can be used to predict the ship production cost.