Quantification Of Defects Research Articles

Rail Web Buried Defect Location and Quantification Methods in Hybrid High-Order Guided Wave Detection

Rail Web Buried Defect Location and Quantification Methods in Hybrid High-Order Guided Wave Detection

A High-Precision Defect Quantification Method Using a Unilateral Oblique Focusing Guided Wave EMAT and a Theory-Informed Explainable Progressive Residual Deep Convolutional Network

A High-Precision Defect Quantification Method Using a Unilateral Oblique Focusing Guided Wave EMAT and a Theory-Informed Explainable Progressive Residual Deep Convolutional Network

Multi-feature vision transformer for automatic defect detection and quantification in composites using thermography

This paper proposes a novel multi-feature vision transformer model for automatic defect detection and quantification in composites using thermography. Firstly, a sampling strategy for infrared data is proposed to address the issues of data class overlap and imbalance. Secondly, multiple feature fusion is employed to aggregate various important infrared features that exhibit strong correlations with defects. Furthermore, a multi-feature vision transformer (VIT) model that leverages self-attention mechanisms to capture global context and long-range dependencies in the composite images is developed. Experiments are performed to verify the effectiveness of the proposed method. The results show that the proposed method has excellent performance in terms of both accuracy and generalization capability.

Multi-Scale Characterization of Porosity and Cracks in Silicon Carbide Cladding after Transient Reactor Test Facility Irradiation

Silicon carbide (SiC) ceramic matrix composite (CMC) cladding is currently being pursued as one of the leading candidates for accident-tolerant fuel (ATF) cladding for light water reactor applications. The morphology of fabrication defects, including the size and shape of voids, is one of the key challenges that impacts cladding performance and guarantees reactor safety. Therefore, quantification of defects’ size, location, distribution, and leak paths is critical to determining SiC CMC in-core performance. This research aims to provide quantitative insight into the defect’s distribution under multi-scale characterization at different length scales before and after different Transient Reactor Test Facility (TREAT) irradiation tests. A non-destructive multi-scale evaluation of irradiated SiC will help to assess critical microstructural defects from production and/or experimental testing to better understand and predict overall cladding performance. X-ray computed tomography (XCT), a non-destructive, data-rich characterization technique, is combined with lower length scale electronic microscopic characterization, which provides microscale morphology and structural characterization. This paper discusses a fully automatic workflow to detect and analyze SiC-SiC defects using image processing techniques on 3D X-ray images. Following the XCT data analysis, advanced characterizations from focused ion beam (FIB) and transmission electron microscopy (TEM) were conducted to verify the findings from the XCT data, especially quantitative results from local nano-scale TEM 3D tomography data, which were utilized to complement the 3D XCT results. In this work, three SiC samples (two irradiated and one unirradiated) provided by General Atomics are investigated. The irradiated samples were irradiated in a way that was expected to induce cracking, and indeed, the automated workflow developed in this work was able to successfully identify and characterize the defects formation in the irradiated samples while detecting no observed cracking in the unirradiated sample. These results demonstrate the value of automated XCT tools to better understand the damage and damage propagation in SiC-SiC structures for nuclear applications.

Quantification analysis of mural defects in digital holography with fundamental voice excitation based on local phase field separation

Previous collaborative studies have shown that fringe patterns by optical interference way are used as a direct diagnostic tool for qualitative analysis of defects in Culture Heritage. Nevertheless, the complexity of the fringe patterns prevents the accurate quantification of defects. In this study, a phase separation method based on the weighted least-squares algorithm is proposed to conveniently isolate the local phase distribution from the whole, complex fringe pattern. This method is examined to accurately quantify the deformation and strain distribution of visible and invisible defects. In experimental work, the Non-destructive Acoustic excitation and Digital Holography (NDA-DH) detection is employed for the superficial and subsurface defects in the Ruting murals of the Forbidden City, Beijing. Under the impact of fundamental human voice, the frequency range of the acoustic excitation was considered from 100–800 Hz. All results show that the suggested phase separation method allows direct observation of defects as well as quantified surface deformation providing a precious documentation to the conservators.

Validating the Use of Conductive Atomic Force Microscopy for Defect Quantification in 2D Materials.

Defects significantly affect the electronic, chemical, mechanical, and optical properties of two-dimensional (2D) materials. Thus, it is critical to develop a method for convenient and reliable defect quantification. Scanning transmission electron microscopy (STEM) and scanning tunneling microscopy (STM) possess the required atomic resolution but have practical disadvantages. Here, we benchmark conductive atomic force microscopy (CAFM) by a direct comparison with STM in the characterization of transition metal dichalcogenides (TMDs). The results conclusively demonstrate that CAFM and STM image identical defects, giving results that are equivalent both qualitatively (defect appearance) and quantitatively (defect density). Further, we confirm that CAFM can achieve single-atom resolution, similar to that of STM, on both bulk and monolayer samples. The validation of CAFM as a facile and accurate tool for defect quantification provides a routine and reliable measurement that can complement other standard characterization techniques.

Computing the characteristics of defects in wooden structures using image processing and CNN

Wood, a time-honored construction material prized for its exceptional properties, has been in use for millennia. Its enduring popularity is attributed to its remarkable strength, aesthetic appeal, and favorable environmental footprint. However, wooden structures are susceptible to various defects and imperfections that pose threats to their structural integrity, durability, and safety. These issues encompass knots, cracks, warping, twisting, decay, insect infestations, and more, all of which, if left unaddressed, can culminate in structural failures. Thus, a comprehensive strategy involving inspection, maintenance, and remediation is indispensable for safeguarding wooden structures. Traditional manual inspections, while effective, are characterized by their resource-intensive nature, entailing significant time and cost investments. This study presents a pioneering approach that leverages Convolutional Neural Networks (CNNs) and Image Processing techniques to revolutionize the assessment of damage in wooden structures using digital imagery. Initially, CNNs are employed to categorize images into three fundamental classes: cracks, knots, and undamaged sections. Subsequently, Image Processing techniques are harnessed to compute precise characteristics of these defects, including parameters such as crack length, width, angle, and the extent of the defective area within knots. The Inception-ResNet-V2 pre-trained model is utilized, fine-tuned and validated with a robust dataset comprising 9000 wooden defect images, evenly distributed across the three aforementioned categories. A prudent division allocates 70% of the dataset for model training, with the remaining 30% reserved for validation. Following successful training, the model demonstrates an impressive overall accuracy of 92% when classifying an independent test set comprising 100 new images. To illustrate the model's performance, two images from each damage category are selected and tested to compute the characteristics of the defects. The quantification error for crack angle is only 0.15%, while it is 0.99% for crack length, and 2% for crack width, demonstrating the high performance of the model. The practical implications of this work are profound. By automating defect assessment in wooden structures, our approach offers significant advantages to industry professionals. It expedites inspections, reduces labor costs, and enhances the accuracy of defect quantification.

Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review.

This paper provides an in-depth overview of Deep Neural Networks and their application in the segmentation and analysis of lung Magnetic Resonance Imaging (MRI) scans, specifically focusing on hyperpolarized gas MRI and the quantification of lung ventilation defects. An in-depth understanding of Deep Neural Networks is presented, laying the groundwork for the exploration of their use in hyperpolarized gas MRI and the quantification of lung ventilation defects. Five distinct studies are examined, each leveraging unique deep learning architectures and data augmentation techniques to optimize model performance. These studies encompass a range of approaches, including the use of 3D Convolutional Neural Networks, cascaded U-Net models, Generative Adversarial Networks, and nnU-net for hyperpolarized gas MRI segmentation. The findings highlight the potential of deep learning methods in the segmentation and analysis of lung MRI scans, emphasizing the need for consensus on lung ventilation segmentation methods.

Deciphering TrFE Fingerprints in P(VDF-TrFE) by Raman Spectroscopy: Defect Quantification and Morphotropic Phase Boundary

Deciphering TrFE Fingerprints in P(VDF-TrFE) by Raman Spectroscopy: Defect Quantification and Morphotropic Phase Boundary

A Motion Deblurring Network for Enhancing UAV Image Quality in Bridge Inspection

Unmanned aerial vehicles (UAVs) have been increasingly utilized for facility safety inspections due to their superior safety, cost effectiveness, and inspection accuracy compared to traditional manpower-based methods. High-resolution images captured by UAVs directly contribute to identifying and quantifying structural defects on facility exteriors, making image quality a critical factor in achieving accurate results. However, motion blur induced by external factors such as vibration, low light conditions, and wind during UAV operation significantly degrades image quality, leading to inaccurate defect detection and quantification. To address this issue, this research proposes a deblurring network using a Generative Adversarial Network (GAN) to eliminate the motion blur effect in UAV images. The GAN-based motion deblur network represents an image inpainting method that leverages generative models to correct blurry artifacts, thereby generating clear images. Unlike previous studies, this proposed approach incorporates deblur and blur learning modules to realistically generate blur images required for training the generative models. The UAV images processed using the motion deblur network are evaluated using a quality assessment method based on local blur map and other well-known image quality assessment (IQA) metrics. Moreover, in the experiment of crack detection utilizing the object detection system, improved detection results are observed when using enhanced images. Overall, this research contributes to improving the quality and accuracy of facility safety inspections conducted with UAV-based inspections by effectively addressing the challenges associated with motion blur effects in UAV-captured images.

A comprehensive study on developing an intelligent framework for identification and quantitative evaluation of the bearing defect size

An intelligent framework is necessary to detect and analyze bearing defects in rotating machinery to prevent unexpected downtime and achieve performance per Industry 4.0 standards. This study presents a framework to detect faults and precisely quantify their size. The framework triggers an AI model to identify the defect and another model for quantitative evaluation of defect size. After analyzing the various classification and regression models, it has been found that The k-nearest neighbor (KNN) algorithm is suggested as the most effective AI model for identifying bearing defects. The ensemble tree is the most effective AI model for defect quantification. The results showed that the proposed algorithm can estimate the defect width reasonably. The maximum error in estimating the inner race, outer race, and roller defect widths was 2.474%, 14.534%, and 5.517%, respectively. The AI model's capacity to identify bearing defects of different sizes, which were not included in the training dataset, was also tested. The test yielded successful results. By using this framework, industries can prevent unexpected downtime, reduce maintenance costs, and enhance the performance and reliability of rotating machinery.

Automated quantification of small defects in ultrasonic phased array imaging using AWGA-gcForest algorithm

ABSTRACT The imaging quality of ultrasonic phased array technology significantly affects the quantification of small defects inside metals. Moreover, traditional manual evaluation methods require a substantial amount of manpower and time, and they are easily influenced by subjective factors. To mitigate the impact of imaging quality on the automated quantification of defects, this study proposes an improved gcForest algorithm (AWGA-gcForest, Adaptive window genetic algorithm- gcForest). Firstly, the acquired ultrasonic full matrix data from the phased array is processed using full focusing A-scan technique. Additionally, a window averaging weighting method is introduced for ultrasound signal processing to eliminate the interference from incident waves and backwall echoes. Subsequently, a multi-scale adaptive sliding window adjustment strategy based on Mean Absolute Deviation (MAD) is employed to reduce the computational time complexity of the gcForest algorithm. Furthermore, a weighted approach optimised with a genetic algorithm is utilised in the cascade forest to improve the classification accuracy and generalisation capability. Experimental results demonstrate that the proposed method achieves an accuracy of 97.50%, precision of 97.26%, recall rate of 96.63%, and F1 score of 96.92, exhibiting higher detection accuracy compared to other models while also reducing time costs.

The Diagnosis of Shunt Defects in CIGS Modules Using Lock-In Thermography: An Empirical Comparative Study

Shunt defects are often detected in solar panels intended for photovoltaic applications. However, existing nondestructive detection technologies have certain inherent drawbacks depending on the application scenario. In this context, this paper reports a comprehensive empirical investigation into lock-in thermography (LIT) and its applicability to diagnosing shunt defects in copper indium gallium selenide (CIGS) solar modules. LIT was compared with biased thermography, and its distinctive attributes were elucidated. The comparison results demonstrate the superior capabilities of LIT at enhancing the signal-to-noise ratio, improving the visibility, resolution, and quantification of defects, and highlighting the usefulness of LIT for advanced defect analysis. We explored scenarios in which biased thermography could be appropriate despite its inherent limitations and identified conditions under which it might be preferred. The complex thermal behavior of different types of defects under various voltage conditions was analyzed, contributing to a more nuanced understanding of their behavior. Thus, integrating experimental results and theoretical understanding, we provide valuable insights and scientific guidelines for photovoltaic research. Our findings could help enhance the efficiency of defect detection in CIGS modules, highlighting the critical role of optimized thermographic techniques in developing photovoltaic technologies.

Unified Quantification of Quantum Defects in Small-Diameter Single-Walled Carbon Nanotubes by Raman Spectroscopy.

The covalent functionalization of single-walled carbon nanotubes (SWCNTs) with luminescent quantum defects enables their application as near-infrared single-photon sources, as optical sensors, and for in vivo tissue imaging. Tuning the emission wavelength and defect density is crucial for these applications. While the former can be controlled by different synthetic protocols and is easily measured, defect densities are still determined as relative rather than absolute values, limiting the comparability between different nanotube batches and chiralities. Here, we present an absolute and unified quantification metric for the defect density in SWCNT samples based on Raman spectroscopy. It is applicable to a range of small-diameter semiconducting nanotubes and for arbitrary laser wavelengths. We observe a clear inverse correlation of the D/G+ ratio increase with nanotube diameter, indicating that curvature effects contribute significantly to the defect activation of Raman modes. Correlation of intermediate frequency modes with defect densities further corroborates their activation by defects and provides additional quantitative metrics for the characterization of functionalized SWCNTs.

Visualization and quantitative evaluation of delamination defects in GFRPs via sparse millimeter-wave imaging and image processing

Multilayer glass fiber reinforced polymer (GFRP) has broad applications, playing a pivotal role in various fields including aerospace and wind energy production. These structures may develop internal delamination defects during their manufacture or service, which can significantly impair the integrity and safety of the equipment. Thus, the inspection of such delamination defects during the service life of the equipment is crucial for assessing its operational performance and lifespan. Millimeter-wave (MMW) nondestructive testing (NDT) presents a promising method for detecting these defects in GFRPs, due to its high resolution and powerful penetration characteristics. This paper proposes a visual NDT and quantitative characterization method based on sparse imaging and image processing. This method encompasses the following three key points. (i) To facilitate rapid detection and visualization in monostatic mode, we proposed a detection approach utilizing sparse scanning and imaging. Initially, we developed a sparse sampling control matrix grounded to guide the probe for spatial automatic sparse sampling, thereby enhancing data acquisition efficiency and reducing data volume. Subsequently, we introduced a rapid sparse imaging method, integrating wave spectrum reconstruction and compressed sensing (CS). Beginning from the original data domain, we designed a fast imaging operator employing forward and inverse transformations of spectrum reconstruction imaging. (ii) In order to further refine the quality of the detection images, we proposed an adaptive singular value decomposition (SVD) approach to suppress the coupling signal and enhance the specimen signal. By adaptively adjusting the singular values, we generated a signal with the optimal target-to-background ratio (TBR). (iii) We ascertained the position and area parameters of the delamination defects using the proposed defect quantification method, combining image segmentation and image statistics. Experimental validation affirmed the effectiveness of the proposed method, revealing the following results: 1) The proposed method, despite utilizing a small amount of observational data, generates better visual images while maintaining efficient detection; 2) It can effectively identify all 0.5 mm-thick internal delamination defects in GFRPs, simultaneously retaining high quantitative characterization precision; 3) The positional quantification error remains less than 0.5 mm, and the relative area quantification error is constrained within 8%.

Introducing "MEW2" Software: A Tool to Analyze MQ-NMR Experiments for Elastomers.

Low-field time-domain proton Nuclear Magnetic Resonance (NMR) spectroscopy is an attractive and powerful tool for studying the structure and dynamics of elastomers. The existence of crosslinks and other topological constraints in rubber matrices (entanglements and filler-rubber interactions, among others) renders the fast segmental fluctuations of the polymeric chains non-isotropic, obtaining nonzero residual dipolar couplings, which is the main observable of MQ-NMR experiments. A new software, Multiple quantum nuclear magnetic resonance analyzer for Elastomeric Networks v2 (MEW2), provides a new tool to facilitate the study of the molecular structure of elastomeric materials. This program quantitatively analyzes two different sets of experimental data obtained in the same experiment, which are dominated by multiple-quantum coherence and polymer dynamics. The proper quantification of non-coupled network defects (dangling chain ends, loops, etc.) allows the analyzer to normalize the multiple quantum intensity, obtaining a build-up curve that contains the structural information without any influence from the rubber dynamics. Finally, it provides the spatial distribution of crosslinks using a fast Tikhonov regularization process based on a statistical criterion. As a general trend, this study provides an automatic solution to a tedious procedure of analysis, demonstrating a new tool that accelerates the calculations of network structure using 1H MQ-NMR low-field time-domain experiments for elastomeric compounds.

Scanning tunneling microscopy for imaging and quantification of defects in as-deposited MoS[formula omitted] monolayers on sapphire substrates

This paper quantitatively assessed the intrinsic defectivity of as-deposited molybdenum disulfide (MoS2) monolayers on sapphire substrates using atomically resolved scanning tunneling microscopy and spectroscopy. We observed two types of point defects — protrusion-like and depression-like with average densities of (2.4 ± 2) ⋅ 1013 cm−2 and (1.9 ± 1.4) ⋅ 1012 cm−2, respectively. The position of the electron state within the MoS2 bandgap suggests that the protrusion-like defect associates with a sulfur mono-vacancy.

Passive Guided Wave Tomography for Erosion Monitoring in an Air/Sand Test Loop

The detection and quantification of defects in pipes by non-destructive testing techniques plays a crucial role in several industries (petrochemical and nuclear in particular). Once a defect is detected, assessing its severity enables to schedule maintenance appropriately, hence reducing maintenance cost and rupture or leakage risks. Structural Health Monitoring (SHM) is an approach that consists in instrumenting a structure with permanent sensors to monitor its health status throughout its life. Guided elastic waves are particularly suitable for SHM applications on thin structures (such as pipes) thanks to their ability to propagate over long distances and their high sensitivity. An SHM approach called "passive guided waves tomography" has emerged in recent years to detect and quantify corrosion in pipes (or plate-like structures). This technology is based on the combination of guided elastic wave tomography algorithms and a passive method such as the so-called ambient noise cross-correlation. It allows to obtain absolute and precise maps of the thickness of an area surrounded by a distribution of sensors without emitting waves, simply by analyzing the elastic noise that exists naturally in the pipe (due to vibrations, fluid-structure interactions with a moving fluid, etc.). In this paper passive guided waves tomography is tested in representative conditions thanks to an erosion loop that circulates air and sand. By impacting the tube wall, sand grains erode the structure, creating thickness losses that we monitor with passive tomography.

Adsorption behavior of ruthenium in tri-n-butyl phosphate functionalized multi walled carbon nanotubes

This paper deals with adsorption behavior of ruthenium on Tri-n-butyl phosphate functionalized Multi Walled Carbon Nanotubes (MWCNTs-COO-TBP) from aqueous solution. Adsorption method is significant for trace levels (expected level) removal of ruthenium in bioassay (urine) samples which is mandatory requirement of radiation workers in nuclear environment. Multi Walled Carbon Nanotubes (MWCNTs) were initially converted to carboxylated Multi Walled Carbon nanotubes (MWCNTs-COOH) and subsequently treated with Tri-n-butyl phosphate to MWCNTs-COO-TBP. The synthesized MWCNTs-COOH and MWCNTs-COO-TBP were confirmed by Fourier Transform Infrared Spectroscopy (FTIR). Spectroscopic and microscopic techniques such as X-Ray Diffraction (XRD), Raman Spectroscopy, Scanning Electron Microscopy-Energy Dispersive X-ray Spectroscopy (SEM-EDS) and High Resolution Transmission Electron Microscopy (HR-TEM) were used for its characterization before and after adsorption. Various adsorption parameters such as contact time, pH, metal ion concentration and temperature were studied. Langmuir adsorption isotherm model and Pseudo second order kinetics model were found to be best fitted in terms of standard deviation and regression coefficient. Thermodynamics parameters such as enthalpy (ΔH°), entropy (ΔS°), Gibbs energy (ΔG°), activation energy (Ea) and sticking probability (SP*) were calculated. The adsorption capacity was calculated and found to be 141.23 mg.g−1.Moreover, quantification of defects, strains with charge doping states were calculated from Raman mapping and correlation plot.

Non-destructive internal disorder segmentation in pear fruit by X-ray radiography and AI

A particular challenge for food quality inspection remains the quantification of internal defects. This work addresses the challenge of internal defects segmentation in pear fruit (cv. Conference) based on high-throughput inline X-ray radiography images in combination with deep learning. Unlike previous approaches, which focused on healthy vs defect classification, the current segmentation approach is able to determine the type of defect, its dimensions, and location. To this end, a novel simulation method was designed to obtain input-target image pairs of radiography data, and more diverse defect pears were generated using a conditioned generator model. The obtained data contributed to the design of a segmentation model that labeled every pixel in the X-ray radiographs of pear fruit as ‘external air’, ‘healthy tissue’, ‘core’, ‘browning’, or ‘cavity’. We demonstrated that additional synthetic data in the learning process drastically improved the model performance. For instance, the mean IoU increased from 0.781 ± 0.112 to 0.883 ± 0.088 for consumable pears with minor cavities and browning. In terms of utility, our segmentation maps provide more detailed information about the type, size, and location of the disorders compared to the heatmaps produced by existing benchmark classifiers.