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Identification of Material Properties of Orthotropic Composite Plate Using Experimental Frequency Response Function Data

机译:使用实验频率响应函数数据识别正交复合板的材料性能

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The demand for composite materials is increasing due to their great superiority in material properties, e.g., lightweight, high strength and high corrosion resistance. As a result, the invention of composite materials of diverse properties is becoming prevalent, and thus, leading to the development of material identification methods for composite materials. Conventional identification methods are destructive, time-consuming and costly. Therefore, an accurate identification approach is proposed to circumvent these drawbacks, involving the use of Frequency Response Function (FRF) error function defined by the correlation discrepancy between experimental and Finite-Element generated FRFs. A square E-glass epoxy composite plate is investigated under several different configurations of boundary conditions. It is notable that the experimental FRFs are used as the correlation reference, such that, during computation, the predicted FRFs are continuously updated with reference to the experimental FRFs until achieving a solution. The final identified elastic properties, namely in-plane elastic moduli, E_x and Ey, in-plane shear modulus, G_(xy), and major Poisson's ratio, v_(xy) of the composite plate are subsequently compared to the benchmark parameters as well as with those obtained using modal-based approach. As compared to the modal-based approach, the proposed method is found to have yielded relatively better results. This can be explained by the direct employment of raw data in the proposed method that avoids errors that might incur during the stage of modal extraction.
机译:由于材料性能的巨大优势,例如,轻质,高强度和高耐腐蚀性,复合材料的需求正在增加。结果,多种性质的复合材料的发明变得普遍,从而导致复合材料材料识别方法的发展。传统的识别方法是破坏性的,耗时的且昂贵的。因此,提出了一种准确的识别方法来规避这些缺点,涉及使用由实验和有限元生成FRF之间的相关差异定义的频率响应函数(FRF)误差函数。在几种不同的边界条件配置下研究方形的E-玻璃环氧复合板。值得注意的是,实验FRF被用作相关参考,使得在计算期间,预测的FRF在实验FRF中连续更新,直到实现解决方案。随后,随后与基准参数相比,最终识别的弹性特性,即面内弹性模,E_X和EY,面内剪切模量,G_(XY)和主要泊松比,V_(XY)以及基准参数。与使用基于模态的方法获得的那些一样。与基于模态的方法相比,发现所提出的方法产生了相对更好的结果。这可以通过在所提出的方法中直接就业原始数据来解释,该方法避免在模态提取阶段可能产生的错误。

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