ESPI non-destructive testing of GRP composite materials containing impact damage

1998 ◽  
Vol 29 (7) ◽  
pp. 721-729 ◽  
Author(s):  
Z.Y. Zhang ◽  
M.O.W. Richardson ◽  
M. Wisheart ◽  
J.R. Tyrer ◽  
J. Petzing
2020 ◽  
Vol 12 (4) ◽  
pp. 168781402091376 ◽  
Author(s):  
Bing Wang ◽  
Shuncong Zhong ◽  
Tung-Lik Lee ◽  
Kevin S Fancey ◽  
Jiawei Mi

Composite materials/structures are advancing in product efficiency, cost-effectiveness and the development of superior specific properties. There are increasing demands in their applications to load-carrying structures in aerospace, wind turbines, transportation, medical equipment and so on. Thus, robust and reliable non-destructive testing of composites is essential to reduce safety concerns and maintenance costs. There have been various non-destructive testing methods built upon different principles for quality assurance during the whole lifecycle of a composite product. This article reviews the most established non-destructive testing techniques for detection and evaluation of defects/damage evolution in composites. These include acoustic emission, ultrasonic testing, infrared thermography, terahertz testing, shearography, digital image correlation, as well as X-ray and neutron imaging. For each non-destructive testing technique, we cover a brief historical background, principles, standard practices, equipment and facilities used for composite research. We also compare and discuss their benefits and limitations and further summarise their capabilities and applications to composite structures. Each non-destructive testing technique has its own potential and rarely achieves a full-scale diagnosis of structural integrity. Future development of non-destructive testing techniques for composites will be directed towards intelligent and automated inspection systems with high accuracy and efficient data processing capabilities.


2019 ◽  
Vol 9 (14) ◽  
pp. 2810 ◽  
Author(s):  
Azadeh Noori Hoshyar ◽  
Maria Rashidi ◽  
Ranjith Liyanapathirana ◽  
Bijan Samali

Monitoring of structures to identify types of damages that occur under loading is essential in practical applications of civil infrastructure. In this paper, we detect and visualize damage based on several non-destructive testing (NDT) methods. A machine learning (ML) approach based on the Support Vector Machine (SVM) method is developed to prevent misdirection of the event interpretation of what is happening in the material. The objective is to identify cracks in the early stages, to reduce the risk of failure in structures. Theoretical and experimental analyses are derived by computing the performance indicators on the smart aggregate (SA)-based sensor data for concrete and reinforced-concrete (RC) beams. Validity assessment of the proposed indices was addressed through a comparative analysis with traditional SVM. The developed ML algorithms are shown to recognize cracks with a higher accuracy than the traditional SVM. Additionally, we propose different algorithms for microwave- or millimeter-wave imaging of steel plates, composite materials, and metal plates, to identify and visualize cracks. The proposed algorithm for steel plates is based on the gradient magnitude in four directions of an image, and is followed by the edge detection technique. Three algorithms were proposed for each of composite materials and metal plates, and are based on 2D fast Fourier transform (FFT) and hybrid fuzzy c-mean techniques, respectively. The proposed algorithms were able to recognize and visualize the cracking incurred in the structure more efficiently than the traditional techniques. The reported results are expected to be beneficial for NDT-based applications, particularly in civil engineering.


2016 ◽  
Author(s):  
Egor V. Yakovlev ◽  
Kirill I. Zaytsev ◽  
Nikita V. Chernomyrdin ◽  
Arsenii A. Gavdush ◽  
Arsen K. Zotov ◽  
...  

2005 ◽  
Vol 02 (01) ◽  
pp. 63-76
Author(s):  
M. Z. ISKANDARANI ◽  
N. F. SHILBAYEH

An innovative NDT (non-destructive testing) technique for interrogating materials for their defects has been developed successfully. The technique has a novel approach to data analysis by employing intensity, RGB signal re-mix and wavelength variation of a thermally generated IR-beam onto the specimen under test which can be sensed and displayed on a computer screen as an image. Specimen inspection and data analysis are carried out through pixel level re-ordering and shelving techniques within a transformed image file using a sequence grouping and regrouping software system, which is specifically developed for this work. The interaction between an impact damaged RIM composite structure and thermal energy is recorded, analyzed, and modeled using an equivalent Electronic circuit. Effect of impact damage on the integrity of the composite structure is also discussed.


Proceedings ◽  
2018 ◽  
Vol 2 (8) ◽  
pp. 554 ◽  
Author(s):  
Mathias Kersemans ◽  
Erik Verboven ◽  
Joost Segers ◽  
Saeid Hedayatrasa ◽  
Wim Van Paepegem

Different non-destructive testing techniques have been evaluated for detecting and assessing damage in carbon fiber reinforced plastics: (i) ultrasonic C-scan, (ii) local defect resonance of front/back surface and (iii) lock-in infrared thermography in reflection. Both artificial defects (flat bottom holes and inserts) and impact damage (barely visible impact damage) have been considered. The ultrasonic C-scans in reflection shows good performance in detecting the defects and in assessing actual defect parameters (e.g., size and depth), but it requires long scanning procedures and water coupling. The local defect resonance technique shows acceptable defect detectability, but has difficulty in extracting actual defect parameters without a priori knowledge. The thermographic inspection is by far the fastest technique, and shows good detectability of shallow defects (depth < 2 mm). Lateral sizing of shallow damage is also possible. The inspection of deeper defects (depth > 3–4 mm) in reflection is problematic and requires advanced post-processing approaches in order to improve the defect contrast to detectable limits.


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