journal article·2012-06

A Comparison of Laser Shearography and C-Scan for Assessing a Glass/Epoxy Laminate Impact Damage

Martin Kadlec, Roman Růžek

Applied Composite Materials· Volume 19· Issue 3-4
Publisher page

Abstract

Impact damage is a serious damage mechanism in composite materials, which limits their performance and reliability. Impact damage can occur during in-service applications or as a result of handling during manufacturing. Methods used currently for damage detection are based on different principles, and for that reason, they give a range of results no matter what the real damage is. Therefore, a comparison of the internal real damage with the flaw indications of a glass fibre–reinforced polymer (GFRP) laminate made with two non-destructive technique (NDT) methods has been investigated. Laser shearography measurements and C-scan ultrasonic detection were compared. Metallographic examination and surface indentation measurements provided information about the character of the real damage. Such a comparison has not yet been published because laser shearography is considered a qualitative technique. Each NDT method was able to visualise a different type of damage. The knowledge of the applicability of these methods is the key to taking advantage of both methods by combining their respective strengths. In terms of the reliability, simplicity and rapidity of all of the mentioned techniques, laser shearography turned out to be the most suitable method for the detection of barely visible flaws. The C-scan was more appropriate for precisely defining the inner damage. The tested material was a laminate typically used for ultralight aircraft. Information about the extent of damage is very important for airplane certification and maintenance.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Thermography and Photoacoustic Techniques · Optical measurement and interference techniques · Ultrasonics and Acoustic Wave Propagation

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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