Acoustic Signal-Based Defect Identification for Directed Energy Deposition-Arc Using Wavelet Time-Frequency Diagrams.

Several advantages of directed energy deposition-arc (DED-arc) have garnered considerable research attention including high deposition rates and low costs. However, defects such as discontinuity and pores may occur during the manufacturing process. Defect identification is the key to monitoring and quality assessments of the additive manufacturing process. This study proposes a novel acoustic signal-based defect identification method for DED-arc via wavelet time-frequency diagrams. With the continuous wavelet transform, one-dimensional (1D) acoustic signals acquired in situ during manufacturing are converted into two-dimensional (2D) time-frequency diagrams to train, validate, and test the convolutional neural network (CNN) models. In this study, several CNN models were examined and compared, including AlexNet, ResNet-18, VGG-16, and MobileNetV3. The accuracy of the models was 96.35%, 97.92%, 97.01%, and 98.31%, respectively. The findings demonstrate that the energy distribution of normal and abnormal acoustic signals has significant differences in both the time and frequency domains. The proposed method is verified to identify defects effectively in the manufacturing process and advance the identification time.

Thickness Characterization of Steel Plate Coating Materials with Terahertz Time-Domain Reflection Spectroscopy Based on BP Neural Network.

Accurate monitoring of steel plate coating thickness is crucial in construction quality control and durability assessments. To address this challenge, this study introduces a terahertz time-domain reflection spectroscopy based on a BP neural network model to achieve a quantitative visualization characterization of coating thickness. The BP neural network eliminates the inherent dependence of terahertz reflection spectroscopy on the refractive index value in thickness calculation. This trained BP neural network model effectively establishes a functional relationship between signal feature parameters and the corresponding thickness values. Additionally, the proposed model can innovatively measure different coating materials' refractive indexes, revealing the corresponding values for the black paint, white paint, epoxy resin, and rubber as 2.212, 1.967, 1.924, and 2.185, respectively. The experimental results demonstrate the trained BP neural network model possesses remarkable accuracy in predicting coating thickness within the scanning area, achieving a precision level exceeding 96%. This method enables the visualization of coating thickness and the extraction of thickness characterization values. Furthermore, using the thickness imaging results as a reference, the method can accurately identify the thickness abnormalities across the scanning area, locating the position and size of potential defects such as internal scratches and foreign object defects. This innovative approach offers a superior means of monitoring and assessing the thickness distribution quality of the steel plate coating layer materials.

An optimized filter design approach for enhancing imaging quality in industrial linear accelerator.

BACKGROUND: The polychromatic X-rays generated by a linear accelerator (Linac) often result in noticeable hardening artifacts in images, posing a significant challenge to accurate defect identification. To address this issue, a simple yet effective approach is to introduce filters at the radiation source outlet. However, current methods are often empirical, lacking scientifically sound metrics. OBJECTIVE: This study introduces an innovative filter design method that optimizes filter performance by balancing the impact of ray intensity and energy on image quality. MATERIALS AND METHODS: Firstly, different spectra under various materials and thicknesses of filters were obtained using GEometry ANd Tracking (Geant4) simulation. Subsequently, these spectra and their corresponding incident photon counts were used as input sources to generate different reconstructed images. By comprehensively comparing the intensity differences and noise in images of defective and non-defective regions, along with considering hardening indicators, the optimal filter was determined. RESULTS: The optimized filter was applied to a Linac-based X-ray computed tomography (CT) detection system designed for identifying defects in graphite materials within high-temperature gas-cooled reactor (HTR), with defect dimensions of 2 mm. After adding the filter, the hardening effect reduced by 22%, and the Defect Contrast Index (DCI) reached 3.226. CONCLUSION: The filter designed based on the parameters of Average Difference (AD) and Defect Contrast Index (DCI) can effectively improve the quality of defect images.

Defect Identification Method for Transformer End Pad Falling Based on Acoustic Stability Feature Analysis.

A transformer's acoustic signal contains rich information. The acoustic signal can be divided into a transient acoustic signal and a steady-state acoustic signal under different operating conditions. In this paper, the vibration mechanism is analyzed, and the acoustic feature is mined based on the transformer end pad falling defect to realize defect identification. Firstly, a quality-spring-damping model is established to analyze the vibration modes and development patterns of the defect. Secondly, short-time Fourier transform is applied to the voiceprint signals, and the time-frequency spectrum is compressed and perceived using Mel filter banks. Thirdly, the time-series spectrum entropy feature extraction algorithm is introduced into the stability calculation, and the algorithm is verified by comparing it with simulated experimental samples. Finally, stability calculations are performed on the voiceprint signal data collected from 162 transformers operating in the field, and the stability distribution is statistically analyzed. The time-series spectrum entropy stability warning threshold is given, and the application value of the threshold is demonstrated by comparing it with actual fault cases.

Automatic Detection and Identification of Defects by Deep Learning Algorithms from Pulsed Thermography Data.

Infrared thermography (IRT), is one of the most interesting techniques to identify different kinds of defects, such as delamination and damage existing for quality management of material. Objective detection and segmentation algorithms in deep learning have been widely applied in image processing, although very rarely in the IRT field. In this paper, spatial deep-learning image processing methods for defect detection and identification were discussed and investigated. The aim in this work is to integrate such deep-learning (DL) models to enable interpretations of thermal images automatically for quality management (QM). That requires achieving a high enough accuracy for each deep-learning method so that they can be used to assist human inspectors based on the training. There are several alternatives of deep Convolutional Neural Networks for detecting the images that were employed in this work. These included: 1. The instance segmentation methods Mask-RCNN (Mask Region-based Convolutional Neural Networks) and Center-Mask; 2. The independent semantic segmentation methods: U-net and Resnet-U-net; 3. The objective localization methods: You Only Look Once (YOLO-v3) and Faster Region-based Convolutional Neural Networks (Fast-er-RCNN). In addition, a regular infrared image segmentation processing combination method (Absolute thermal contrast (ATC) and global threshold) was introduced for comparison. A series of academic samples composed of different materials and containing artificial defects of different shapes and nature (flat-bottom holes, Teflon inserts) were evaluated, and all results were studied to evaluate the efficacy and performance of the proposed algorithms.

Defect Detection Method of Carbon Fiber Sucker Rod Based on Multi-Sensor Information Fusion and DBN Model.

Because of its unique characteristics of small specific gravity, high strength, and corrosion resistance, the carbon fiber sucker rod has been widely used in petroleum production. However, there is still a lack of corresponding online testing methods to detect its integrity during the process of manufacturing. Ultrasonic nondestructive testing has become one of the most accepted methods for inspection of homogeneous and fixed-thickness composites, or layered and fixed-interface-shape composites, but a carbon fiber sucker rod with multi-layered structures and irregular interlayer interfaces increases the difficulty of testing. In this paper, a novel defect detection method based on multi-sensor information fusion and a deep belief network (DBN) model was proposed to identify online its defects. A water-immersed ultrasonic array with 32 ultrasonic probes was designed to realize the online and full-coverage scanning of carbon fiber rods in radial and axial positions. Then, a multi-sensor information fusion method was proposed to integrate amplitudes and times-of-flight of the received ultrasonic pulse-echo signals with the spatial angle information of each probe into defect images with obvious defects including small cracks, transverse cracks, holes, and chapped cracks. Three geometric features and two texture features from the defect images characterizing the four types of defects were extracted. Finally, a DBN-based defect identification model was constructed and trained to identify the four types of defects of the carbon fiber rods. The testing results showed that the defect identification accuracy of the proposed method was 95.11%.

Internal Cylinder Identification Based on Different Transmission of Longitudinal and Shear Ultrasonic Waves.

We have built a Fizeau fiber interferometer to investigate the internal cylindrical defects in an aluminum plate based on laser ultrasonic techniques. The ultrasound is excited in the plate by a Q-switched Nd:YAG laser. When the ultrasonic waves interact with the internal defects, the transmitted amplitudes of longitudinal and shear waves are different. The experimental results show that the difference in transmission amplitudes can be attributed to the high frequency damping of internal cylinders. When the scanning point is close to the internal defect, the longitudinal waves attenuate significantly in the whole defect area, and their amplitude is always smaller than that of shear waves. By comparing the transmitted amplitudes of longitudinal and shear waves at different scanning points, we can achieve a C scan image of the sample to realize the visual inspection of internal defects. Our system exhibits outstanding performance in detecting internal cylinders, which could be used not only in evaluating structure cracks but also in exploring ultrasonic transmission characteristics.

High-Sensitivity Ultrasonic Guided Wave Monitoring of Pipe Defects Using Adaptive Principal Component Analysis.

Ultrasonic guided wave monitoring is regularly used for monitoring the structural health of industrial pipes, but small defects are difficult to identify owing to the influence of the environment and pipe structure on the guided wave signal. In this paper, a high-sensitivity monitoring algorithm based on adaptive principal component analysis (APCA) for defects of pipes is proposed, which calculates the sensitivity index of the signals and optimizes the process of selecting principal components in principal component analysis (PCA). Furthermore, we established a comprehensive damage index (K) by extracting the subspace features of signals to display the existence of defects intuitively. The damage monitoring algorithm was tested by the dataset collected from several pipe types, and the experimental results show that the APCA method can monitor the hole defect of 0.075% cross section loss ratio (SLR) on the straight pipe, 0.15% SLR on the spiral pipe, and 0.18% SLR on the bent pipe, which is superior to conventional methods such as optimal baseline subtraction (OBS) and average Euclidean distance (AED). The results of the damage index curve obtained by the algorithm clearly showed the change trend of defects; moreover, the contribution rate of the K index roughly showed the location of the defects.

Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure.

Due to the increased service life, environmental corrosion, unreasonable construction, and other issues, local defects inevitably exist in civil structures, which affect the structural performance and can lead to structural failure. However, research on grout defect identification of precast reinforced concrete frame structures with rebars spliced by sleeves faces great challenges owing to the complexity of the problem. This study presents a multiple-variable spatiotemporal regression model algorithm to identify local defects based on structural vibration responses collected using a sensor network. First, numerical simulations were carried out on precast beam-column connection models by comparing the identification results based on a single-variable regression model, two-variable spatial regression model, and two-variable spatiotemporal regression model; furthermore, a multiple-variable spatiotemporal regression model was proposed and robustness analysis of the damage indicator was carried out. Then, to explore the validity of the proposed method, a nondestructive vibration experiment was considered on a half-scaled, two-floor, precast concrete frame structure with column rebars spliced by defective grout sleeves. The results show that local defects were successfully identified based on a multiple-variable spatiotemporal regression model.

Weak Defect Identification for Centrifugal Compressor Blade Crack Based on Pressure Sensors and Genetic Algorithm.

The Centrifugal compressor is a piece of key equipment for petrochemical factories. As the core component of a compressor, the blades suffer periodic vibration and flow induced excitation mechanism, which will lead to the occurrence of crack defect. Moreover, the induced blade defect usually has a serious impact on the normal operation of compressors and the safety of operators. Therefore, an effective blade crack identification method is particularly important for the reliable operation of compressors. Conventional non-destructive testing and evaluation (NDT&E) methods can detect the blade defect effectively, however, the compressors should shut down during the testing process which is time-consuming and costly. In addition, it can be known these methods are not suitable for the long-term on-line condition monitoring and cannot identify the blade defect in time. Therefore, the effective on-line condition monitoring and weak defect identification method should be further studied and proposed. Considering the blade vibration information is difficult to measure directly, pressure sensors mounted on the casing are used to sample airflow pressure pulsation signal on-line near the rotating impeller for the purpose of monitoring the blade condition indirectly in this paper. A big problem is that the blade abnormal vibration amplitude induced by the crack is always small and this feature information will be much weaker in the pressure signal. Therefore, it is usually difficult to identify blade defect characteristic frequency embedded in pressure pulsation signal by general signal processing methods due to the weakness of the feature information and the interference of strong noise. In this paper, continuous wavelet transform (CWT) is used to pre-process the sampled signal first. Then, the method of bistable stochastic resonance (SR) based on Woods-Saxon and Gaussian (WSG) potential is applied to enhance the weak characteristic frequency contained in the pressure pulsation signal. Genetic algorithm (GA) is used to obtain optimal parameters for this SR system to improve its feature enhancement performance. The analysis result of experimental signal shows the validity of the proposed method for the enhancement and identification of weak defect characteristic. In the end, strain test is carried out to further verify the accuracy and reliability of the analysis result obtained by pressure pulsation signal.

Defect identification of bare printed circuit boards based on Bayesian fusion of multi-scale features.

The aim of this article is to propose a defect identification method for bare printed circuit boards (PCB) based on multi-feature fusion. This article establishes a description method for various features of grayscale, texture, and deep semantics of bare PCB images. First, the multi-scale directional projection feature, the multi-scale grey scale co-occurrence matrix feature, and the multi-scale gradient directional information entropy feature of PCB were extracted to build the shallow features of defect images. Then, based on migration learning, the feature extraction network of the pre-trained Visual Geometry Group16 (VGG-16) convolutional neural network model was used to extract the deep semantic feature of the bare PCB images. A multi-feature fusion method based on principal component analysis and Bayesian theory was established. The shallow image feature was then fused with the deep semantic feature, which improved the ability of feature vectors to characterize defects. Finally, the feature vectors were input as feature sequences to support vector machines for training, which completed the classification and recognition of bare PCB defects. Experimental results show that the algorithm integrating deep features and multi-scale shallow features had a high recognition rate for bare PCB defects, with an accuracy rate of over 99%.

Nondestructive Inspection and Quantification of Select Interface Defects in Honeycomb Sandwich Panels.

Honeycomb sandwich panels are utilized in many industrial applications due to their high bending resistance relative to their weight. Defects between the core and the facesheet compromise their integrity and efficiency due to the inability to transfer loads. The material system studied in the present paper is a unidirectional carbon fiber composite facesheet with a honeycomb core with a variety of defects at the interface between the two material systems. Current nondestructive techniques focus on defect detectability, whereas the presented method uses high-frequency ultrasound testing (UT) to detect and quantify the defect geometry and defect type. Testing is performed using two approaches, a laboratory scale immersion tank and a novel portable UT system, both of which utilize only single-side access to the part. Coupons are presented with defects spanning from 5 to 40 mm in diameter, whereas defects in the range of 15-25 mm and smaller are considered below the detectability limits of existing inspection methods. Defect types studied include missing adhesive, unintentional foreign objects that occur during the manufacturing process, damaged core, and removed core sections. An algorithm is presented to quantify the defect perimeter. The provided results demonstrate successful defect detection, with an average defect diameter error of 0.6 mm across all coupons studied in the immersion system and 1.1 mm for the portable system. The best accuracy comes from the missing adhesive coupons, with an average error of 0.3 mm. Conversely, the worst results come from the missing or damaged honeycomb coupons, with an error average of 0.7 mm, well below the standard detectability levels of 15-25 mm.

Polarization Dynamics of Solid-State Quantum Emitters.

Quantum emitters in solid-state crystals have recently attracted a great deal of attention due to their simple applicability in optical quantum technologies. The polarization of single photons generated by quantum emitters is one of the key parameters that plays a crucial role in various applications, such as quantum computation, which uses the indistinguishability of photons. However, the degree of single-photon polarization is typically quantified using the time-averaged photoluminescence intensity of single emitters, which provides limited information about the dipole properties in solids. In this work, we use single defects in hexagonal boron nitride and nanodiamond as efficient room-temperature single-photon sources to reveal the origin and temporal evolution of the dipole orientation in solid-state quantum emitters. The angles of the excitation and emission dipoles relative to the crystal axes were determined experimentally and then calculated using density functional theory, which resulted in characteristic angles for every specific defect that can be used as an efficient tool for defect identification and understanding their atomic structure. Moreover, the temporal polarization dynamics revealed a strongly modified linear polarization visibility that depends on the excited-state decay time of the individual excitation. This effect can potentially be traced back to the excitation of excess charges in the local crystal environment. Understanding such hidden time-dependent mechanisms can further improve the performance of polarization-sensitive experiments, particularly that for quantum communication with single-photon emitters.

A novel two-level approach to defect detection in braided CFRP using air-coupled ultrasonic testing.

Air-coupled ultrasonic testing and C-scan technique has been increasingly applied to the braided CFRP structures owing to its non-destruction, non-contact and high visualization characteristics. Due to the noise, structural vibration, and airflow in the process of detection, the accuracy of defect identification is easily deteriorated. To address this issue and further determine the relationship between the ultrasonic acoustical pressure attenuation and structural parameters, a novel two-level identification method based on the modified two-dimensional variational mode decomposition (2D-VMD) has been proposed. In the first level, C-scan images have been sparsely decomposed into ensembles of modes by 2D-VMD method. Then, the modes have been screened by mutual information method to realize the reconstruction of new image in the second level. Experimental results have shown that the proposed method has the good ability to identify defects with a minimum detectable diameter of 1-2 mm. It has been noted that the ultrasonic acoustical pressure attenuation has become remarkably higher in the twill weave CFRP than the plain weave CFRP and the ratio of pressure attenuation between two weave types of CFRP has decreased with the defect depth increase. Meanwhile, shadows around defects in C-scan images have been suppressed to a great extent. It has been demonstrated that the capability of denoising has enabled the developed method with the accurate detection in terms of the shape, size, depth and weave type. With these advantages, the proposed method has provided valuable insights into the development of an effective method for defect detection of braided CFRP structures.

Wavelet Scattering and Neural Networks for Railhead Defect Identification.

Accurate and automatic railhead inspection is crucial for the operational safety of railway systems. Deep learning on visual images is effective in the automatic detection of railhead defects, but either intensive data requirements or ignoring defect sizes reduce its applicability. This paper developed a machine learning framework based on wavelet scattering networks (WSNs) and neural networks (NNs) for identifying railhead defects. WSNs are functionally equivalent to deep convolutional neural networks while containing no parameters, thus suitable for non-intensive datasets. NNs can restore location and size information. The publicly available rail surface discrete defects (RSDD) datasets were analyzed, including 67 Type-I railhead images acquired from express tracks and 128 Type-II images captured from ordinary/heavy haul tracks. The ultimate validation accuracy reached 99.80% and 99.44%, respectively. WSNs can extract implicit signal features, and the support vector machine classifier can improve the learning accuracy of NNs by over 6%. Three criteria, namely the precision, recall, and F-measure, were calculated for comparison with the literature. At the pixel level, the developed approach achieved three criteria of around 90%, outperforming former methods. At the defect level, the recall rates reached 100%, indicating all labeled defects were identified. The precision rates were around 75%, affected by the insignificant misidentified speckles (smaller than 20 pixels). Nonetheless, the developed learning framework was effective in identifying railhead defects.

Enhanced identification of defective pre-coated paints on metal through simple temperature-perturbed infrared measurement in conjunction with two-trace two-dimensional correlation analysis.

A promising infrared (IR) spectroscopic method able to effectively identify defective pre-coated metal (PCM), a pre-painted metal panel, has been demonstrated. A temperature-perturbed IR measurement in conjunction with a two-trace two-dimensional (2T2D) correlation analysis was proposed as a strategy for enhancing defect identification. Our objectives were to induce dissimilar temperature-driven structural variations of base paints and added components, to recognize dissimilarities by 2T2D correlation analysis, and to use subsequent 2T2D correlation features to identify sample defects. For the exploratory examination, three defect cases were studied: 1) grey-silver PCMs with and without phosphate epoxy (2.0%), 2) normal and violet colorant-contaminated (0.2%) black PCMs, and 3) normal, violet (0.5%), and yellow colorant-contaminated (0.1%) white PCMs. The IR spectral features of the PCMs collected at 20 and 50 °C were different due to the temperature-dependent structural variations. Initial measurements at 50 °C allowed discrimination of normal and violet colorant-contaminated black PCMs. When using 2T2D slice spectra obtained from 2T2D correlation analysis using the spectra measured at the two temperatures, violet- as well as yellow colorant-contaminated white PCMs were identified, while these were unclear in the measurements at either 20 or 50 °C. The effective capture of dissimilar temperature-driven spectral variations of base paint and colorants (contaminants) by 2T2D correlation analysis was responsible for the improved defect identification.

An improved Faster R-CNN for defect recognition of key components of transmission line.

In a national power grid system, it is necessary to keep transmission lines secure. Detection and identification must be regularly performed for transmission tower components. In this paper, we propose a defect recognition method for key components of transmission lines based on deep learning. First, based on the characteristics of the transmission line image, the defect images are preprocessed, and the defect dataset is created. Then, based on the TensorFlow platform and the traditional Faster R-CNN based on the R-CNN model, the concept-ResNet-v2 network is used as the basic feature extraction network to improve the network structure adjustment and parameter optimization. Through feature extraction, target location, and target classification of aerial transmission line defect images, a target detection model is obtained. The model improves the feature extraction on transmission line targets and small target component defects. The experimental results show that the proposed method can effectively identify the defects of key components of the transmission lines with a high accuracy of 98.65%.

Defects in CsPbX3 Perovskite: From Understanding to Effective Manipulation for High-Performance Solar Cells.

The rapid development of all inorganic metal perovskite (CsPbX3 , X represents halogen) materials holds great promise for top-cells in tandem junctions due to their glorious thermal stability and continuous adjustable band gap in a wide range. Due to the presence of defects, the power conversion efficiency (PCE) of CsPbX3 perovskite solar cells (PSCs) is still substantially below the Shockley-Queisser (SQ) limit. Therefore, it is imperative to have an in-depth understanding of the defects in PSCs, thus to evaluate their impact on device performances and to develop corresponding strategies to manipulate defects in PSCs for further promoting their photoelectric properties. In this review, the latest progress in defect passivation in the CsPbX3 PSCs field is summarized. Starting from the effect of non-radiative recombination on open circuit voltage (Voc ) losses, the defect physics, tolerance, self-healing, and the effect of defects on the photovoltaic properties are discussed. Some techniques to identify defects are compared based on quantitative and qualitative analysis. Then, passivation manipulation is discussed in detail, the defect passivation mechanisms are proposed, and the passivation agents in CsPbX3 thin films are classified. Finally, directions for future research about defect manipulation that will push the field to progress forward are outlined.

Defect identification and statistics toolbox: automated defect analysis for scanning probe microscopy images.

Identifying and classifying defects in scanning probe microscopy (SPM) images is an important task that is tedious to perform by hand. In this paper we present the defect identification and statistics toolbox (DIST), an image processing toolbox for identifying and analyzing atomic defects in SPM images. DIST combines automation with user input to accurately and efficiently identify defects and automatically compute critical statistics. We describe using DIST for interactive image processing, generating contour plots for isolating extrema from an image background, and processes for identifying defects.

An innovative multivariate strategy for HSI-NIR images to automatically detect defects in green coffee.

In the present study, an advanced and original multivariate strategy for the processing of hyperspectral images in the near-infrared region is proposed to automatically detect physico-chemical defects in green coffee, which are similar one to each other by naked eye. An object-based approach for the characterization of individual beans, rather than single pixels, was adopted, calculating a series of descriptive parameters characterizing the distribution of scores on the lowest-order principal components. On such parameters, the k-nearest neighbors (k-NN) classification algorithm was applied and the predictive results on the test samples indicate that this approach is able not only to distinguish defective beans from non-defective ones, but also to differentiate the various types of defects. Hyperspectral imaging is demonstrated to be a valid alternative for the sorting of green beans - a crucial phase for coffee import/export.