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A damage pattern recognition method for hypervelocity impact on aluminum honeycomb core sandwich based on acoustic emission
Received date: 2016-05-05
Revised date: 2017-02-13
Online published: 2017-03-03
Supported by
National Special Project for Space Debris during the Twelfth Five-year Plan Period (K0203210);the Fundamental Research Funds for Central Universities (HIT.NSRIF.2015029)
A damage pattern recognition method based on neural network is proposed to recognize the damage state of aluminum honeycomb core sandwich under hypervelocity impact (HVI) through acoustic emission. A variety of experimental signals are obtained, 10 characteristic parameters related to damage are presented by test of nonparametric analysis the relationship with damage pattern, combining with precise source localization, time-frequency analysis, wavelet transformation and modal acoustic emission technology. The BP neural net mode based on Bayesian regularization is established by analyzing the relationship with damage pattern using nonparametric analysis. After establishing the optimal network model, two optimal combinations are selected by analyzing the recognition ability of different parameter combinations, the damage pattern recognition ability is verified with non-same source sample. The result shows that propagation distance is a significant parameter but irrelevant to damage pattern. Automatic window wavelet energy ratio within 125-250 kHz frequency range decrease the ability of damage pattern recognition. Using a Bayesian regularization neural network with combination of 9 parameters, including propagation distance, rise time, hold time, cut-off frequency, 4 kinds of automatic window wavelet energy ratio and wavelet energy entropy, presents 9.38% wrong point rate to a group of random non-same source sample.
LIU Yuan , PANG Baojun , CHI Runqiang , CAI Yuan . A damage pattern recognition method for hypervelocity impact on aluminum honeycomb core sandwich based on acoustic emission[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2017 , 38(5) : 220401 -220401 . DOI: 10.7527/S1000-6893.2017.220401
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