Identification of an effective nondestructive technique for bond defect determination in laminate composites—A technical review

Laminate composites are commonly used for the production of critical mechanical structures and components such as wind turbine blades, helicopter rotors, unmanned aerial vehicle wings and honeycomb structures for aircraft wings. During the manufacturing process of these composite structures, zones or areas with weak bond strength are always issues, which may affect the strength and performance of components. The identification and quantification of these zones are always challenging and necessary for the mass production. Non-destructive testing methods available, including ultrasonic A, B, and C-Scan, laser shearography, X-ray tomography, and thermography can be useful for the mentioned purposes. A comparison of these techniques concerning their capacity of identification and quantification of bond defects; however, still needs a comprehensive review. In this paper, a detailed comparison of several non-destructive testing techniques is provided. Emphasis is placed to institute a guideline to select the most suitable technique for the identification of zones with bond defects in laminated composites. Experimental tests on different composite based machined components are also discussed in detail. The discussion provides practical evidence about the effectiveness of different non-destructive testing techniques.N/

A Statistical Framework for Improved Automatic Flaw Detection in Nondestructive Evaluation Images

Nondestructive evaluation (NDE) techniques are widely used to detect flaws in critical components of systems like aircraft engines, nuclear power plants and oil pipelines in order to prevent catastrophic events. Many modern NDE systems generate image data. In some applications an experienced inspector performs the tedious task of visually examining every image to provide accurate conclusions about the existence of flaws. This approach is labor-intensive and can cause misses due to operator ennui. Automated evaluation methods seek to eliminate human-factors variability and improve throughput. Simple methods based on peak amplitude in an image are sometimes employed and a trained-operator-controlled refinement that uses a dynamic threshold based on signal-to-noise ratio (SNR) has also been implemented. We develop an automated and optimized detection procedure that mimics these operations. The primary goal of our methodology is to reduce the number of images requiring expert visual evaluation by filtering out images that are overwhelmingly definitive on the existence or absence of a flaw. We use an appropriate model for the observed values of the SNR-detection criterion to estimate the probability of detection. Our methodology outperforms current methods in terms of its ability to detect flaws