Detecting Defects in Composite Polymers by Using 3D Scanning Laser Doppler Vibrometry

Materials (Basel). 2022 Oct 14;15(20):7176. doi: 10.3390/ma15207176.

Abstract

The technique of 3D scanning laser Doppler vibrometry has recently appeared as a promising tool of nondestructive evaluation of discontinuity-like defects in composite polymers. The use of the phenomenon of local defect resonance (LDR) allows intensifying vibrations in defect zones, which can reliably be detected by means of laser vibrometry. The resonance acoustic stimulation of structural defects in materials causes compression/tension deformations, which are essentially lower than the material tensile strength, thus proving a nondestructive character of the LDR technique. In this study, the propagation of elastic waves in composites and their interaction with structural inhomogeneities were analyzed by performing 3D scanning of vibrations in Fast Fourier Transform mode. At each scanning point, the in-plane (x, y) and out of plane (z) vibration components were analyzed. The acoustic stimulation was fulfilled by generating a frequency-modulated harmonic signal in the range from 50 Hz to 100 kHz. In the case of a reference plate with a flat bottom hole, the resonance frequencies for all (x, y, and z) components were identical. In the case of impact damage in a carbon fiber reinforced plastic sample, the predominant contribution into total vibrations was provided by compression/tension deformations (x, y vibration component) to compare with vibrations by the z coordinate. In general, inspection results were enhanced by analyzing total vibration patterns obtained by averaging results at some resonance frequencies.

Keywords: 3D scanning laser Doppler vibrometry; local defect resonance; nondestructive testing; polymer composite.

Grants and funding

This study was supported by [Tomsk Polytechnic University Development Program] grant number [Priority 2030-NIP-EB-008-0000-2022] (scientific equipment), the [Russian Foundation for Basic Research] grant no. [19-29-13004] (experimental results), a grant from the [Russian Science Foundation] no. [22-19-00103] (theoretical part), and [Sevastopol State University] Research grant [42-01-09/169/2021-4] (data processing).