Simulation-Based Capacitive Imaging for Internal Defect Detection in Dielectric Materials

Application of KPCA-based infrared thermal wave radar imaging in the detection of internal defects in carbon steel materials

The development of a method for detecting material defects using graph-based manifold ranking and heterogeneous image features is considered in the article. The aim of the research is to develop a method that allows high-precision detection of defect in images with low color contrast of detecting and background areas. The image is pre-segmented into regions to calculate the saliency map. The graph is based on regions. The defect is determined based on background area queries. The areas adjacent to the edges of the image belong to the background areas. Color features of the image are used in the existing approach based on the manifold ranking. Texture features are used in the proposed method to improve accuracy. Gabor's energy function is used to calculate texture features. The proposed method has shown good results for detection of material defect in images in which the background color and object color are in similar ranges. The experimental results are presented on test images. Precision-recall curves showing the advantage of the developed method are constructed.

Neutron radiography (NR) technique was applied to analyze behaviors of thermal neutrons in slabs, which have a wide range of the ratio r, of scattering to non-scattering cross sections. Transmittances were measured for various materials as basic data for NR study. Scattering components were estimated and subtracted from observed neutron intensities measured behind slabs. Effective total macroscopic cross sections were estimated and were found they agreed with given values of ASTM E748-80 within ±20%. Defects (voids and impurities) were also analyzed, and their detection limits for thickness t were estimated for materials of various r as a function of t.

AI enhanced strong-field terahertz spectral detection and imaging

Application of terahertz time domain spectroscopy for NDT of oxide-oxide ceramic matrix composites

The problem of dipole radiation in or near planar layered media is of significance to many fields of study. It is encountered in antenna theory, single-molecule spectroscopy, cavity quantum electrodynamics, integrated optics, circuit design (microstrips), and surface-contamination control. The relevant theory was also applied to explain the strongly enhanced Raman effect of adsorbed molecules on noble metal surfaces, and in surface science and electrochemistry for the study of optical properties of molecular systems adsorbed on solid surfaces. Detailed literature on the latter topic is given in Ref. [1]. In the context of nano-optics, dipoles close to a planar interface have been considered by various authors to simulate tiny light sources and small scattering particles [2]. The acoustic analog is also applied to a number of problems such as seismic investigations and ultrasonic detection of defects in materials [3].

Generating ultrasonic images indistinguishable from real images using Generative Adversarial Networks

COST is a framework for scientific and technical cooperation, allowing the coordination of national research on a European level, including 32 member countries. Participation of institutes from non-COST countries is possible. From an initial 7 Actions in 1971, COST has grown to 200 Actions at the beginning of 2000. COST Action 524 is under materials domain, the title of which being “Neutron Imaging Techniques for the Detection of Defects in Materials”, under the Chairmanship of Dr. P. Chirco (I.N.F.N.). The following countries are represented in the Management Committee of Action 524: Italy, France, Austria, Germany, United Kingdom, Hungary, Switzerland, Spain, Czech Republic, Slovenia, and Russia. The six working groups of this Action are working respectively on standardization of neutron radiography techniques, on aerospace application, on civil engineering applications, on comparison and integration of neutron imaging techniques with other NDT, on neutron tomography, and on non radiographic techniques such as neutron scattering techniques. A specific effort is devoted to standardization issues, with respect to other non European standards. Results of work performed in the COST frame are published or will be published in the review INSIGHT, edited by the British Institute of Non Destructive Testing.

A new Compton backscatter imaging (CBI) technique, described as lateral migration radiography (LMR), has been developed and applied successfully to two difficult diagnostic problems: Detection of buried, plastic landmines, and detection of material flaws which lie close to, and parallel to, a surface, the method is based on image contrast generated by alteration of photon lateral migration relative to the illuminating beam direction. It is extraordinarily sensitive to density and/or atomic number variation along the photon lateral-direction travel paths. In LMR, relevant information-carrying photon detection efficiencies are two to three orders-of-magnitude greater than other CBI techniques such that the electric energy requirement for x-ray generation is only about one joule per acquired image pixel. The resulting small product of pixel illumination dwell time and x-ray generator electric power implies that current, easily accessible technology can be used to fabricate LMR systems with practical usage protocols. Three have been designed and built at the University of Florida: A laboratory device for perfecting buried landmine acquisition; a mobile system for field-demonstrating landmine detection; and, a laboratory system for detection of material defects in small structural parts. The LMR images, acquired in a laboratory landmine detection setting, are so definitive that identification of the mine-type, as well as presence, can be often accomplished. Results of a field test are near-perfect, both in determining buried landmine presence and in lack of false positive response. Images acquired in material flaw detection indicate ability to detect lateral cracks or delaminations with thickness less than 100 microns, as well as corrosion on surfaces between layers of structural sheets. These applications provide evidence of the viability of a new, one-sided x-ray radiography technique which images hidden structures of objects which have here-to-fore been difficult, or impossible, to detect with practical image aquisition times.

Pulsed thermography (PT) has emerged as a widely adopted technology for internal defect detection in carbon fibre reinforced polymer (CFRP) composites owing to its cost-effectiveness and rapid detection capabilities. However, defect identification in PT testing remains challenging because of the significant non-uniform heat distribution background and noise interference within the thermal images. This study introduces a novel thermographic defect detection approach that combines a multi-scale wavelet transform with sparse principal component thermography (MWT-SPCT). In this method, a multi-scale wavelet transform (MWT) is first applied to the thermal images for band-pass filtering. By leveraging the multi-resolution analysis capability of wavelet transform, the MWT simultaneously eliminates low-frequency background and high-frequency noise components. Following this stage, SPCT is employed to extract the features of defects based on the dynamic variation of defect signals throughout the sequence. Finally, through binarization of the extracted features, accurate defect segmentation and localization can be obtained. The experimental results demonstrated that, the MWT preprocessing selectively isolates defect information, thus increasing the signal-to-noise values of the defect signals in the filtered results. Compared to SPCT frameworks incorporating preprocessing methods such as contrast enhancement, background equalization, and signal smoothing, the proposed MWT SPCT framework yields significantly improved detection performance. Moreover, generalization evaluations demonstrate that MWT SPCT maintains consistently high accuracy and strong statistical stability across repeated measurements under different inspection conditions. This validates the reliability of the MWT-SPCT framework for defect detection under the influence of background and noise interference.

We have used optical coherence tomography to study the internal structure of a variety of non-biological materials. In particular, we have imaged internal regions from a commercial grade of lead zirconate titanate ceramic material, from a sample of single-crystal silicon carbide, and from a Teflon-coated wire. In each case the spatial positions of internal defects were determined.

Ultrasound is a common tool in the nondestructive evaluation of composite material integrity. Echoes from high frequency (5-10 MHz) sound waves vary with subsurface flaws and delaminations. To improve detection of internal defects in composite materials, a linear Bayes classification is applied to the wavelet transform coefficients of ultrasound scan lines. Using a Daubechies basis, wavelet transforms are taken of the ultrasound signals. A subset of the coefficients is then used as features for the classifier. A forward sequential feature selection (FSFS) algorithm was used to determine the optimal features. The training set was comprised of scanned signals from both damaged and undamaged samples. Performance statistics for classification of damaged materials were calculated using a separate set of test samples. Application of a shift-invariant wavelet transform removed some of the variability of the wavelet coefficients and improved the classification.

Most existing carbon fiber composite materials are formed by high-temperature molding of multiple layers of fiber cloth. During the manufacturing and usage processes, materials are prone to defects such as voids, delamination, and inclusions, which seriously threaten their service life and safety performance. Ultrasonic testing is currently a widely adopted method for detecting defects in carbon fiber composite materials. However, existing narrow-pulse ultrasonic transducers often have to sacrifice emission energy to achieve narrow-pulse emission, which results in their limited ability to penetrate thicker carbon fiber composite materials. To address this issue, this paper proposes the design of a focused laminated transducer. By stacking and bonding lead titanate piezoelectric wafers and using a concave lens made of organic glass to focus ultrasonic waves, the emission sound intensity of the ultrasonic transducer is enhanced. The simulation results show that the designed focused double-stack transducer has a directivity gain that is 4.49 dB higher than that of the traditional single-piezoelectric-wafer transducer. The transducer fabricated based on this design has successfully achieved effective detection of internal defects in carbon fiber composite materials.

Magneto acoustic emission technique: A review of methodology, applications, and future prospects in non-destructive testing

Steel is widely used in the aerospace, machinery and automotive industries. Surface defects not only have a negative impact on the appearance of steel but also significantly reduce its wear resistance, high temperature resistance, corrosion resistance and fatigue strength. Therefore, the detection of steel surface defects is very important to improve the quality of steel production. The limited availability of surface defect samples in the industrial sector poses significant challenges for the accurate detection of defects in high-quality materials. In addition, the existing defect detection model is highly complex and not easy to deploy. To solve this problem, a lightweight defect detection network suitable for steel defects is proposed. The cross-layer feature fusion (CFF) in the design enables effective utilisation of multi-layer semantic features, facilitating the detection of small defects in steel. Secondly, a new loss function is designed to make up for the problems of small data volume and uneven data distribution. The experimental results demonstrate that the steel surface defect detection method proposed in this paper achieves the highest detection performance on widely used public datasets such as RSDDS, NEUS and NRSD-CR(test), while maintaining the lowest model complexity.