» Articles » PMID: 38730809

High-Resolution Ultrasound to Quantify Sub-Surface Wrinkles in a Woven CFRP Laminate

Overview
Publisher MDPI
Date 2024 May 11
PMID 38730809
Authors
Affiliations
Soon will be listed here.
Abstract

Carbon fiber reinforced polymer (CFRP) composites are popular materials in the aerospace and automotive industries because of their low weight, high strength, and corrosion resistance. However, wrinkles or geometric distortions in the composite layers significantly reduce their mechanical performance and structural integrity. This paper presents a method for non-destructively extracting the three-dimensional geometry, lamina by lamina, of a laminated composite. A method is introduced for fabricating consistent out-of-plane wrinkled CFRP laminate panels, simulating the in-service wrinkle observed in industries that utilize thick structure composites such as the vertical lift or wind power industries. The individual lamina geometries are extracted from the fabricated coupon with an embedded wrinkle from captured ultrasonic waveforms generated from single-element conventional ultrasonic (UT) scan data. From the extracted waveforms, a method is presented to characterize the wrinkle features within each individual lamina, specifically the spatially varying wrinkle height and intensity for the wrinkle. Parts were fabricated with visibly undetectable wrinkles using a wet layup process and a hot press for curing. Scans were performed in a conventional immersion tank scanning system, and the scan data were analyzed for wrinkle detection and characterization. Extraction of the layers was performed based on tracking the voltage peaks from A-scans in the time domain. Spatial Gaussian averaging was performed to smooth the A-scans, from which the surfaces were extracted for each individual lamina. The extracted winkle surface aligned with the anticipated wrinkle geometry, and a single parameter for quantification of the wrinkle intensity for each lamina is presented.

Citing Articles

Ultrasonic Phased Array Testing and Identification of Multiple-Type Internal Defects in Carbon Fiber Reinforced Plastics Based on Convolutional Neural Network.

Ma M, Wang Z, Gao Z, Jiang M Materials (Basel). 2025; 18(2).

PMID: 39859789 PMC: 11766589. DOI: 10.3390/ma18020318.


Automated Foreign Object Detection for Carbon Fiber Laminates Using High-Resolution Ultrasound Testing.

Nargis R, Pulipati D, Jack D Materials (Basel). 2024; 17(10).

PMID: 38793448 PMC: 11123281. DOI: 10.3390/ma17102381.

References
1.
Katunin A, Wronkowicz-Katunin A, Dragan K . Impact Damage Evaluation in Composite Structures Based on Fusion of Results of Ultrasonic Testing and X-ray Computed Tomography. Sensors (Basel). 2020; 20(7). PMC: 7180951. DOI: 10.3390/s20071867. View

2.
Koodalil D, Rajagopal P, Balasubramaniam K . Quantifying adhesive thickness and adhesion parameters using higher-order SH guided waves. Ultrasonics. 2021; 114:106429. DOI: 10.1016/j.ultras.2021.106429. View

3.
Blackman N, Jack D, Blandford B . Improvement in the Quantification of Foreign Object Defects in Carbon Fiber Laminates Using Immersion Pulse-Echo Ultrasound. Materials (Basel). 2021; 14(11). PMC: 8198476. DOI: 10.3390/ma14112919. View