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基于模态分析和BP神经网络的复合材料脱层损伤监测研究
引用本文:王宏涛,刘利锋,周来水,郑世杰. 基于模态分析和BP神经网络的复合材料脱层损伤监测研究[J]. 中国机械工程, 2005, 16(3): 239-242,248
作者姓名:王宏涛  刘利锋  周来水  郑世杰
作者单位:南京航空航天大学,南京,210016
基金项目:国家自然科学基金资助项目(10072026),江苏省自然科学基金资助项目(BK2002090)
摘    要:基于假定自然应变法建立了一个新的八节点压电固体单元,并采用具有相同坐标但不同节点号的节点对模拟脱层,分析了含不同脱层损伤梁的模态特性;进而提出了一种将计算力学、神经网络和实验模态分析相结合的复合材料结构脱层损伤检测的新方法。该方法通过数值模拟的手段为神经网络提供充足的训练样本,以实验模态结果作为神经网络的输入来预测复合材料结构的脱层损伤,实验结果证明了这一方法的可行性。

关 键 词:BP神经网络 脱层 假定自然应变单元 损伤检测
文章编号:1004-132X(2005)03-0239-04

Investigation of Delamination Detection for Composite Structures Based on Modal Analysis and BP Neural Network
Wang Hongtao Liu Lifeng Zhou Laishui Zheng Shijie Nanjing University of Aeronautics and Astronautics,Nanjing. Investigation of Delamination Detection for Composite Structures Based on Modal Analysis and BP Neural Network[J]. China Mechanical Engineering, 2005, 16(3): 239-242,248
Authors:Wang Hongtao Liu Lifeng Zhou Laishui Zheng Shijie Nanjing University of Aeronautics  Astronautics  Nanjing
Affiliation:Wang Hongtao Liu Lifeng Zhou Laishui Zheng Shijie Nanjing University of Aeronautics and Astronautics,Nanjing,210016
Abstract:An novel Assumed Natural Strain(ANS) piezoelectric solid element formulation was developed to analyze composite beam with different delamination size and location by using pairs of nodes with the same coordinates but different node numbers. Furthermore, a new method combining computational mechanics, neural network and experimental modal analysis was demonstrated for composite health monitoring. The numerical results obtained by FEM were used to train the neural network and the experimental modal frequencies were input to the neural network to predict the demalination location and extent.
Keywords:BP neural network  delamination  assumed natural strain element  damage detection
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