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.
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Wei, Z., Wang, H., Qiu, Y. (2008). Model-Based Neural Network and Wavelet Packets Decomposition on Damage Detecting of Composites. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2008. Communications in Computer and Information Science, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85930-7_42
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DOI: https://doi.org/10.1007/978-3-540-85930-7_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85929-1
Online ISBN: 978-3-540-85930-7
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