基于局部缺陷共振的CFRPs缺陷图像增强检测方法

Saman Hadi, Reza P. R. Hasanzadeh, M. Kersemans
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摘要

目前,碳纤维增强聚合物(CFRP)等复合材料在工业上得到了广泛应用。但是,它们很容易受到冲击损伤和随后的疲劳开裂和分层,这在长期内会导致一些负面后果,如侵蚀和破坏材料。由于无法直观地观察到这些缺陷,以及工业部件对侵入式检测的高灵敏度,因此使用无损检测技术来处理上述问题。在这方面,一种基于超声的无损检测技术,称为局部缺陷共振(LDR),在检测cfrp中各种类型的缺陷方面取得了显著的效果。在LDR技术中,使用高频声振动来获得缺陷区域的局部共振激活,使得这些激励频率导致缺陷区域相对于声区域的振动幅值显着增加。由此产生的问题是,为了正确地定位缺陷,必须知道缺陷的共振频率,这实际上是不可能的。本文提出了一种新的缺陷成像方法,该方法可以在不知道缺陷位置和共振频率的情况下对缺陷进行定位。以具有平底孔(FBH)缺陷的CFRP试样为实验对象,采用信噪比(SNR)准则对该方法进行了定量验证。结果表明,该方法优于一些已知的算法。
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A Defect Image Enhancement Approach for Detection of Defective Area in CFRPs Through Local Defect Resonance
Nowadays composite materials such as carbon fiber reinforced polymers (CFRP)s have been widely used in industrial applications. But, they are susceptible to impact damage and subsequent fatigue cracking and delamination which in long term lead to some negative consequences such as erosion and also breaking the material. Due to the inability to visually observe such defects and also the high sensitivity of industrial components to invasive inspections, non-destructive testing (NDT) techniques are used to deal with the aforementioned problems. In this regards, an ultrasound-based NDT technique called Local defect resonance (LDR) leads to remarkable results for detecting various types of defects in CFRPs. In LDR technique, high frequency acoustical vibrations are used to get a localized resonant activation of a defective region such that these excitation frequencies lead to a significant increase of the vibration amplitude in the defective area relative to the sound area. The problem which arises is that in order to properly localize the defect, the defect resonance frequency must be known which is practically impossible. In this paper, a new defect imaging methodology is proposed, which can localize the defects without any prior knowledge about their location and resonance frequencies. Experiments are performed on a CFRP sample with flat bottom hole (FBH) defects and the proposed method has been quantitatively validated through the experiments by using the signal-to-noise ratio (SNR) criterion. The results show the superiority of our method over some well-known algorithms.
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