A Defect Image Enhancement Approach for Detection of Defective Area in CFRPs Through Local Defect Resonance
Paper ID : 1118-MVIP2020
Saman Hadi *1, Reza PR-Hasanzadeh2, Mathias Kersemans3
1Dept. of electrical eng. University of Guilan
3Dep. of materials sci.and eng. Ghent University Ghent, Belgium
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 aforemen-tioned problems. In this regards, an ultrasound-based NDT technique called Local defect resonance (LDR) leads to re-markable results for detecting various types of defects in CFRPs. In LDR technique, high frequency acoustical vibra-tions are used to get a localized resonant activation of a defec-tive 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 reso-nance frequency must be known which is practically impossi-ble. In this paper, a new defect imaging methodology is pro-posed, which can localize the defects without any prior knowledge about their location and resonance frequencies. Experiments are performed on a CFRP sample with flat bot-tom 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.
Carbon fiber reinforced polymer (CFRP), non-destructive test-ing (NDT), Local defect resonance (LDR), flat bottom hole (FBH), Defect image enhancement.
Status : Paper Accepted (Oral Presentation)