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.

非均匀结构材料涉及复合材料和粘合/焊接材料。开发它们是为了通过结合几种不同的材料来实现更好的特征。与普通的均匀材料相比，这种非均匀材料将具有控制宏观材料特性的其他参数，即材料特性的差异，混合比率，混合方法等。因此，设计，评估和建模这种非均匀材料变得非常复杂且困难。通过几位研究人员之间的合作作品，该项目研究了通过结合神经网络和计算力学设计，评估和建模非官方材料的创新方法。这项研究的主要结果如下。（1）通过将神经网络和用于随机分布的粒子模型的神经网络和微力力学结合，对热弹性区域中功能分级的材料（FGM）的组成关系进行了很好的建模。（2）针对超声和电势下降方法开发了基于神经网络的非破坏性裂纹检测方法。它们成功地用于检测三维表面裂纹和倾斜的缺陷。（3）成功应用了基于神经网络的逆分析方法，以测量其本征频率和模式来识别纤维增强复合梁的损伤面积。（4）使用Quadtree技术对粗糙形状进行层次建模，并使用编码的边界表示技术将其转换为列表结构，最后将其转换为使用神经网络的简单形状模型。