On acoustic emission for failure investigation in CFRP: Pattern recognition and peak frequency analyses
On acoustic emission for failure investigation in CFRP: Pattern recognition and peak frequency analyses
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DOI：
10.1016/j.ymssp.2010.11.014
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发表时间：
2011-05-01
影响因子：
8.4
通讯作者：
Curtis, P. T.
中科院分区：
文献类型：
--
作者：
Gutkin, R.;Green, C. J.;Curtis, P. T.
本文采用声发射技术研究了碳纤维增强塑料（CFRP）的失效问题.采集了拉伸、紧凑拉伸（CT）、紧凑压缩（CC）、双悬臂梁（DCB）和四点弯曲端部切口弯曲（4-ENF）等不同试验构型的信号并进行后处理，采用三种不同的模式识别算法：k-均值、自组织映射（SOM）结合k-均值和竞争神经网络（CNN）对信号进行分析。SOM与k-means相结合的出现作为最有效的三种算法。聚类分析的结果遵循峰值频率分布的模式，然后对每个测试的频率内容进行详细研究，并实现了几种故障模式的分类。(C)2010爱思唯尔有限公司保留所有权利。
This paper investigates failure in Carbon Fibre Reinforced Plastics CFRP using Acoustic Emission (AE). Signals have been collected and post-processed for various test configurations: tension, Compact Tension (CT), Compact Compression (CC), Double Cantilever Beam (DCB) and four-point bend End Notched Flexure (4-ENF).The signals are analysed with three different pattern recognition algorithms: k-means, Self Organising Map (SOM) combined with k-means and Competitive Neural Network (CNN). The SOM combined with k-means appears as the most effective of the three algorithms. The results from the clustering analysis follow patterns found in the peak frequencies distribution.A detailed study of the frequency content of each test is then performed and the classification of several failure modes is achieved. (C) 2010 Elsevier Ltd. All rights reserved.