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EFFECT OF LOADING PATH AND SPECIMEN SHAPE ON INVERSE IDENTIFICATION OF ELASTIC PROPERTIES OF COMPOSITES

机译:加载路径和试样形状对复合材料弹性性能逆向识别的影响

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摘要

This paper presents a methodology for using sensor-generated data from multi-degree-of-freedom mechatronic loading systems to identify the elastic moduli of a composite laminate material system. This is done not to demonstrate the method itself but rather to study how various features of the experimental and analytical procedure can affect the identification process. The analytical formulation of the identification problem is described first given the geometry of a test specimen and a loading path. The concept of singular value decomposition (associated with pseudo inversion factorization) is introduced for the purpose of parameter identification as it applies to the elastic moduli. Also, the concepts of distinguishability and uniqueness are introduced to evaluate the quality of parameter identification. The analysis is performed on a continuum model basis in order to evaluate if the proposed technique can work for specimens of arbitrary shape. Elastic coefficients were identified using pseudo-experimental (numerically synthesized) data created by finite element analysis (FEA) and the effect of the introduced distinguishability and uniqueness on the identification was investigated through several numerical examples. The effects of the form of the chosen multidimensional loading path(s) and the shape of the specimen on various features of the inverse identification process related to the elastic moduli parameter estimation are also determined. The results of the numerical studies demonstrate the efficacy of the proposed methodology and suggests subsequent avenues for optimizing the specification of the loading path both a priori and in real-time.
机译:本文提出了一种方法,该方法用于使用来自多自由度机电加载系统的传感器生成的数据来识别复合层压材料系统的弹性模量。这样做并不是为了演示方法本身,而是为了研究实验和分析程序的各种功能如何影响识别过程。首先根据试样的几何形状和加载路径来描述识别问题的分析公式。引入奇异值分解(与伪逆因式分解相关联)的概念是出于参数识别的目的,因为它适用于弹性模量。另外,引入了可区分性和唯一性的概念以评估参数识别的质量。分析是在连续模型基础上进行的,以便评估所提出的技术是否可以用于任意形状的样本。使用通过有限元分析(FEA)创建的伪实验(数字合成)数据识别弹性系数,并通过几个数值示例研究引入的可区分性和唯一性对识别的影响。还确定了选定的多维加载路径的形式和样本的形状对与弹性模量参数估计有关的逆识别过程的各个特征的影响。数值研究的结果证明了所提出方法的有效性,并提出了先验和实时优化装载路径规格的后续途径。

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