Application of Neural Networks to Design, Evaluation and Modeling of Nonhomogeneous Structural Materials

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Nonhomogeneous structural materials involve composite materials and bonded/welded materials. They are developed in order to realize better characteristics by combining several different materials. Compared with ordinary homogeneous materials, such nonhomogeneous materials would have some additional parameters controlling macroscopic material properties, i.e. difference of material properties, mixture ratio, mixing methods and so on. Thus it becomes very complicated and difficult to design, evaluate and model such nonhomogeneous materials. Through co-operative works among several researchers, this project investigated innovative methods for designing, evaluating and modeling nonhomogenous materials by combining neural networks and computational mechanics.Principal results of this study are as follows. (1) A constitutive relation of functionally graded material (FGM) in a thermal elastoplastic region is well modeled by combining neural networks and micromechanics for randomly distributed particle model.(2) Neural network based nondestructive crack detection methods were developed for ultrasonics and electric potential drop methods. They were successfuly applied to detect three dimensional surface cracks and inclined defects. (3) A neural network based inverse analysis method was successfully applied to identify damage area of fiber reinforced composite beam by measuring its eigen frequencies and modes. (4) Rough shape is hierarchically modeled using a quadtree technique, and is transformed into list structure using the coded boundary representation technique, and finally is converted into a simple shape model using neural networks.