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Model-Based Neural Network and Wavelet Packets Decomposition on Damage Detecting of Composites

  • Zhi Wei
  • Huisen Wang
  • Ying Qiu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 15)

Abstract

Model-based neural network (MBNN) is used along with wavelet packets decomposition to detect internal or hidden damage in composites. In consideration of internal delaminations with different sizes and locations typical finite element model is used to acquire training data of neural networks. Delamination-induced energy variations are decomposed by wavelet packets to enhance damage features. The predicted delamination size and location are selected as output of neural networks. In order to acquire target signals, forced vibration test is conducted. Based on experimental result, damage-induced energy variation of response signal is analyzed and the relationship between damage and physical performance is related. Test result shows that the proposed method is effective to investigate internal damage state in composites.

Keywords

MBNN composites delamination detecting wavelet packets 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Zhi Wei
    • 1
  • Huisen Wang
    • 2
  • Ying Qiu
    • 1
  1. 1.School of Mechanical EngineeringHebei University of TechnologyTianjinChina
  2. 2.Tianjin Navigation Instruments Research Institute TianjinChina

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