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

神经网络在非均质结构材料设计、评估和建模中的应用

基本信息

批准号：
05302029
负责人：
YAGAWA Genki
金额：
$ 6.78万
依托单位：
University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Co-operative Research (A)
财政年份：
1993
资助国家：
日本
起止时间：
1993 至 1995
项目状态：
已结题

项目摘要

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.

非均质结构材料包括复合材料和粘接/焊接材料。它们是通过组合几种不同的材料来实现更好的特性而开发的。与一般的均匀材料相比，这种非均匀材料会有一些额外的参数来控制宏观材料的性质，即材料性质的不同、配合比、混合方法等。因此，设计、评价和模拟这种非均匀材料变得非常复杂和困难。通过多个研究人员的合作，本项目研究了神经网络和计算力学相结合的非均匀材料设计、评价和建模的创新方法。(1)采用神经网络和细观力学相结合的随机分布颗粒模型，较好地模拟了热弹塑性区功能梯度材料(FGM)的本构关系。(2)提出了基于神经网络的超声和电位降裂纹无损检测方法。它们成功地应用于三维表面裂纹和倾斜缺陷的检测。(3)通过测量纤维增强复合材料梁的固有频率和振型，成功地将基于神经网络的逆分析方法应用于纤维增强复合材料梁的损伤识别。(4)利用四叉树技术对粗略形状进行分层建模，利用编码边界表示技术将粗略形状转化为链表结构，最后利用神经网络将粗略形状转化为简单形状模型。