Failure strength prediction of glass/epoxy composite laminates from acoustic emission parameters using artificial neural network
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- 作者：C. Suresh Kumara ; V. Arumugama ; R. Sengottuvelusamyb ; S. Srinivasanb ; H.N. Dhakalc ; hom.dhakal@port.ac.uk" class="auth_mail" title="E-mail the corresponding author
- 关键词：GFRP composite laminates ; Acoustic emission ; Seawater degradation ; Artificial neural network ; RBFNN ; GRNN
- 刊名：Applied Acoustics
- 出版年：2017
- 出版时间：1 January 2017
- 年：2017
- 卷：115
- 期：Complete
- 页码：32-41
- 全文大小：2865 K
文摘
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Developed RBFNN and GRNN models with higher accuracy and quicker convergence when compared with other supervised learning network.
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The normalized acoustic emission (AE) cumulative parameters are considered for developing the models.
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The input AE parameters are considered only in the constant region (500–800 ms) of every 1% of ultimate load (40–70%) for minimising an error.
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The mean and median of predicted failure load is also computed and compared with experimental failure load.
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Degradation behavior of glass/epoxy composite laminates exposed to seawater environment has hardly been investigated.