Defect imaging in carbon fiber composites by acoustic shearography

Carbon fiber reinforced polymer (CFRP) composites are increasingly used in aerospace, automobile, marine and power industries due to its good mechanical strength and low material density. The defect detection in CFRP has been challenging because of their complex material configuration and various ty...

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Bibliographic Details
Published in:Composites science and technology 2022-05, Vol.223, p.109417, Article 109417
Main Authors: Zhang, Lei, Tham, Zi Wen, Chen, Yi Fan, Tan, Chin Yaw, Cui, Fangsen, Mutiargo, Bisma, Ke, Lin
Format: Article
Language:English
Subjects:
Acoustic shearography
Carbon fiber reinforced plastics
Carbon fibers
Composite materials
Compression tests
Computed tomography
Defects
Fiber composites
Fiber reinforced polymers
Imaging
Laminate
Non-destructive testing
Polymer-matrix composites (PMCs)
Polymers
Shearography
Synchronism
Tomography
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Summary:Carbon fiber reinforced polymer (CFRP) composites are increasingly used in aerospace, automobile, marine and power industries due to its good mechanical strength and low material density. The defect detection in CFRP has been challenging because of their complex material configuration and various types of defects. In this paper, we reported the use of acoustic shearography imaging for defect detection in carbon fiber composite materials. CFRP specimens with different levels of defects were prepared by open-hole compression tests according to ASTM standard. These specimens were subsequently tested with acoustic shearography imaging method. It is found that acoustic shearography can successfully image various defects efficiently. By synchronizing the stroboscopic laser with the ultrasonic wave frequency, the defect imaging quality can be significantly improved. Comparison with X-ray computed tomography results shows that acoustic shearography can provide sufficiently good defect imaging results with significantly reduced imaging time. [Display omitted]
ISSN:0266-3538
1879-1050
DOI:10.1016/j.compscitech.2022.109417