Identification Of Defects Research Articles

Defect detection within filling layer of subway slab ballastless track based on impulse response method

The impulse response method, as a mechanical impedance analysis method with high-impact energy, can provide a rapid assessment of structural integrity. For this reason, the impulse response method is applied to subway slab ballastless tracks, and the theory behind this method, as well as the applicability of detecting the voids within the self-compacting concrete (SCC) filling layer, are deeply studied. The theoretical derivation of the mobility function of subway slab ballastless track under impact load is performed. The finite element model is established and the propagation of elastic waves in the process of impulse response method is simulated. The study also investigates the sensitivity of various characteristic parameters and the frequency analysis range to void identification in the SCC filling layer. Furthermore, the characteristic parameters for defect identification in the SCC filling layer are constructed and subsequently validated through a full-scale test. The results reveal that the impulse response method is capable of generating elastic waves with higher energy and longer wavelengths within the structure. This characteristic is advantageous for the detection of concealed defects in the multi-layer structure. The mobility spectrum of subway slab ballastless track exhibits multi-frequency peak characteristics, and the calculated cut-off frequency of the peak-mean mobility ratio should be extended to more than 1600 Hz. Among the three characteristic parameters of peak mobility, dynamic stiffness, and peak-mean mobility ratio, the peak-mean mobility ratio has the advantage of accurately identifying the void position and providing a more precise range.

Did You Really Test That Interlock?: The Importance of Early Identification of Defects

Did You Really Test That Interlock?: The Importance of Early Identification of Defects

Identification of Defects in Products Made from Honeycomb Composite Materials Using Infrared Scanning Thermography

Identification of Defects in Products Made from Honeycomb Composite Materials Using Infrared Scanning Thermography

A Water Environment-Based Simulated Method for Ultrasonic Testing of Slag Inclusion Weld Defects Based on Improved VMD.

The identification of slag inclusion defects in welds is of the utmost importance in guaranteeing the integrity, safety, and prolonged service life of welded structures. Most research focuses on different kinds of weld defects, but branch research on categories of slag inclusion material is limited and critical for safeguarding the quality of engineering and the well-being of personnel. To address this issue, we design a simulated method using ultrasonic testing to identify the inclusion of material categories in austenitic stainless steel. It is based on a simulated experiment in a water environment, and six categories of cubic specimens, including four metallic and two non-metallic materials, are selected to simulate the slag materials of the inclusion defects. Variational mode decomposition optimized by particle swarm optimization is employed for ultrasonic signals denoising. Moreover, the phase spectrum of the denoised signal is utilized to extract the phase characteristic of the echo signal from the water-slag specimen interface. The experimental results show that our method has the characteristics of appropriate decomposition and good denoising performance. Compared with famous signal denoising algorithms, the proposed method extracted the lowest number of intrinsic mode functions from the echo signal with the highest signal-to-noise ratio and lowest normalized cross-correlation among all of the comparative algorithms in signal denoising of weld slag inclusion defects. Finally, the phase spectrum can ascertain whether the slag inclusion is a thicker or thinner medium compared with the weld base material based on the half-wave loss existing or not in the echo signal phase.

Research on Automated Fiber Placement Surface Defect Detection Based on Improved YOLOv7

Due to the black and glossy appearance of the carbon fiber prepreg bundle surface, the accurate identification of surface defects in automated fiber placement (AFP) presents a high level of difficulty. Currently, the enhanced YOLOv7 algorithm demonstrates certain performance advantages in this detection task, yet issues with missed detections, false alarms, and low confidence levels persist. Therefore, this study proposes an improved YOLOv7 algorithm to further enhance the performance and generalization of surface defect detection in AFP. Firstly, to enhance the model’s feature extraction capability, the BiFormer attention mechanism is introduced to make the model pay more attention to small target defects, thereby improving feature discriminability. Next, the AFPN structure is used to replace the PAFPN at the neck layer to strengthen feature fusion, preserve semantic information to a greater extent, and finely integrate multi-scale features. Finally, WIoU is adopted to replace CIoU as the bounding box regression loss function, making it more sensitive to small targets, enabling more accurate prediction of object bounding boxes, and enhancing the model’s detection accuracy and generalization capability. Through a series of ablation experiments, the improved YOLOv7 shows a 10.5% increase in mAP and a 14 FPS increase in frame rate, with a maximum detection speed of 35 m/min during the AFP process, meeting the requirements of online detection and thus being able to be applied to surface defect detection in AFP operations.

Advancing Deep Learning Techniques for Accurate Identification of Battery Defects

Advancing Deep Learning Techniques for Accurate Identification of Battery Defects

Methods for Passivating Defects of Perovskite for Inverted Perovskite Solar Cells and Modules

AbstractInverted perovskite solar cells (PSCs) have attracted considerable attention due to their distinct advantages, including minimal hysteresis, cost‐effectiveness, and suitability for tandem applications. Nevertheless, the solution processing and the low formation energy of perovskites inevitably lead to numerous defects formed at both the bulk and interfaces of the perovskite layer. These defects can act as non‐radiative recombination centers, significantly impeding carrier transport and posing a substantial obstacle to stability and further enhancing power conversion efficiency (PCE). This review delves into a detailed discussion of the nature and origin of defects and the characterization techniques employed for defect identification. Furthermore, it systematically summarizes methods for defect detection and approaches for passivating interface and bulk defects within the perovskite film in inverted PSCs. Finally, this review offers a perspective on employing upscaling defect passivation engineering for perovskite modules. It is hoped this review provides insights into defect passivation in inverted PSCs and solar modules.

An Improved YOLOv5x-Based Algorithm for IC Pin Welding Defects Detection

This study suggests an integrated circuit (IC) pin welding defect detection algorithm based on improved YOLOv5x to address the issues of low detection accuracy caused by small target size and dense pin arrangement in IC pin welding defects identification. The ability of the network to extract features is improved by effective fusing of features with various receptive fields through the inclusion of the D-SPP module to merge different channel information. The introduction of the mask self-attention mechanism module increases the network’s capacity to recognize global feature correlations and raises the algorithm’s detection precision. In order to speed up the model’s convergence and tackle the issue of sample imbalance in BBox regression, the Focal-EIoU loss function is applied. The detection accuracy and speed are increased by using the k-means++ clustering algorithm to create nine clustering centers to figure out the size of the prior box. According to the results of the experiment, the new method achieves average precisions for short-circuit, missing pin, pin-cocked, and little tin faults in IC pin welding of 96.7%, 94.5%, 95.6%, and 93.3%, respectively. The mean average precision increases to 95.0% at a threshold of 0.5, which is 13.3% and 8.9% greater than YOLOv3 and YOLOv5x, respectively. A reference value for IC pin welding defect identification is provided by the improved algorithm, which has a detection time of 0.142 seconds per image. This meets the speed requirements of IC quality inspection.

OCT monitoring data processing method of laser deep penetration welding based on HDBSCAN

To address the challenge of low accuracy in the measurement of laser deep penetration welding depth, this study introduces optical coherence tomography (OCT) technology for the direct measurement of this parameter, which is designed to enable real-time measurement and the accurate acquisition of depth information. This study has developed a laser welding depth inspection system that integrates Spectral-Domain OCT (SD-OCT), utilizing 304 stainless steel as the test material for inspecting laser deep penetration welding. A comparative analysis evaluated the impact of Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and K-means clustering (K-mean) on the clustering of OCT raw data, in addition to the use of percentile filters combined with the exponential moving average method for extracting the depth-of-melt curve. The depth-of-melt curve, obtained via the HDBSCAN algorithm, more accurately reflects the actual depth curve, with an average error remaining below 5%. This error rate is 46% lower than that of DBSCAN and 42% lower than that of K-mean. The results of the experiments with continuously varying process parameters show that a high degree of accuracy can be demonstrated even when the welding process parameters are continuously varied. Experimental results indicate that the combination of the HDBSCAN algorithm with percentile filtering and the exponential moving average method significantly enhances the recognition accuracy of the deep penetration laser welding depth curve. This method provides more precise feature parameters for the real-time detection and identification of defects in deep penetration laser welding, thereby offering strong support for optimizing and controlling the laser welding process.

REN-GAN: Generative adversarial network-driven rebar clutter elimination network in GPR image for tunnel defect identification

In ground penetrating radar (GPR) images, the signals from reinforcing steel bars (rebars) can significantly obscure the signals of void defects beneath them, which hinders defect detection in tunnel linings. This study introduces a novel, end-to-end unsupervised deep learning-based network that is trained on unpaired GPR images. The primary objective is to suppress the rebar signal and enhance the accuracy of void defect identification in recorded GPR B-Scan images. The network incorporates an autoencoder generator with feature attention and feature mix-up modules, designed to recover textural information and generate improved images devoid of rebar interference. Crucially, this network leverages a strategy of comparing images in feature space, rather than pixel space. The features extracted by the autoencoder are constrained by cycle perceptual consistency loss, derived from a pretrained VGG19 network, to ensure fidelity and perceptual accuracy. Furthermore, this study introduces an enhanced Faster-RCNN network, specifically improved through a more robust feature extraction module, for detecting void defects beneath rebars. The effectiveness of the proposed method has been thoroughly validated using both synthetic and real-world data sets. The results indicate that our method not only surpasses other unsupervised techniques in performance but also effectively diminishes rebar signals and accurately reconstructs and identify void defect signals.

Threshold displacement energy map of Frenkel pair generation in [formula omitted]-Ga2O3 from machine-learning-driven molecular dynamics simulations

β-gallium oxide (β-Ga2O3) shows great promise for electronics applications, particularly, in future space operating devices exposed to harsh radiation environments for extended times. This study focuses on crucial, yet not fully explored, aspects of radiation damage in this material, such as threshold displacement energies and formation of various radiation-induced Frenkel pairs. Analyzing over 5,000 molecular dynamics simulations based on our machine-learning potentials, we conclude that the threshold displacement energies for two Ga sites, the tetrahedral (22.9 eV) and octahedral (20 eV) ones, differ stronger than the same values for three different O sites, which range only between 17 eV and 17.4 eV. Mapping of threshold displacement energies unveils significant differences in displacements for all five atomic sites. Our newly developed defect identification methodology successfully classified multiple Frenkel pair types in β-Ga2O3, with over ten different Ga and two primary O ones with a predominant O split interstitial at the O1 site. Finally, the calculated recombination energy barriers suggest that O Frenkel pairs are more likely to recombine upon annealing than Ga. These insights are pivotal for understanding the radiation damage and defect formation in Ga2O3, providing the basis for design of Ga2O3-based electronics with high radiation resistance.

A reflectance-correction retinex framework for thermal image enhancement in nondestructive defect detection of CFRP

To ensure the quality of materials, pulsed thermography (PT) is an effective technique to detect the presence of internal defects in carbon fiber reinforced polymers (CFRP). However, non-uniform thermal excitation during the testing often leads to uneven heat distribution within the original thermal images, and the limited heat absorption by the surface of specimen may cause low-contrast defect signals that further impair the identification of defects. In response to these challenges, this study introduces an enhancement framework, termed Reflectance-Correction Retinex (RCR). In the RCR framework, the single-scale Retinex method is first applied to equalize the non-uniform background within a thermal image, and a sigmoid modification is proposed to adaptively amplify the contrast between defects and their background. The RCR framework is tested on PT images of man-made CFRP specimens. The experimental results show that our proposed method substantially enhances the visual quality of PT images, which facilitates precision and reliability in defect detection applications.

Detection and segmentation of wire rope surface deficiency based on YOLOv8 and U-Net

Abstract The presence of surface defects in wire ropes (WR) may lead to potential safety hazards and performance degradation, necessitating timely detection and repair. Hence, this paper proposes a method for detecting surface defects in WR based on the deep learning models YOLOv8s and U-Net, aiming to identify surface defects in real-time and extract defect data, thereby enhancing the efficiency of surface defect detection. Firstly, the ECA attention mechanism is incorporated into the YOLOv8 algorithm to enhance detection performance, achieving real-time localization and identification of surface defects in WR. Secondly, in order to obtain detailed defect data, the U-Net semantic segmentation algorithm is employed for morphological segmentation of defects, thereby obtaining the contour features of surface defects. Finally, in conjunction with OpenCV technology, the segmentation results of the defects are quantified to extract data, obtaining parameters such as the area and perimeter of the surface defects in the WR. Experimental results demonstrate that the improved YOLOv8-ECA model exhibits good accuracy and robustness, with the model’s mAP@0.5 reaching 84.78%, an increase of 1.13% compared to the base model, an accuracy rate of 90.70%, and an FPS of 65. The U-Net model can efficiently perform segmentation processing on surface defects of WR, with an mIOU of 83.54% and an mPA of 90.78%. This method can rapidly, accurately, and specifically detect surface defects in WR, which is of significant importance in preventing industrial production safety accidents.

Bird Droppings Defects Detection in Photovoltaic Modules Based on CA-YOLOv5

Due to the characteristics of different forms and small bird droppings-related defects in photovoltaic modules, problems of missing detection, wrong detection, and low detection accuracy often exist in the bird droppings covering the detection of photovoltaic modules. In this paper, a defect identification method based on the improved Coordinate Attention—You Only Look Once (CA-YOLOv5) network is proposed. Firstly, a layer of the Coordinate Attention module is added between the Backbone and Neck network, which takes into account channel and location information to enhance the feature extraction ability of the network model. Secondly, the small target detection layer is added to fuse different feature information from shallow networks and deep networks so as to improve the multi-scale feature detection ability of the network structure and effectively improve the detection effect of small targets. Finally, the experimental results show that, compared with the original algorithm, the proposed improved algorithm based on YOLOv5s has a mean average precision (mAP) value of 92%, an increase of 5.2%, and the model volume is also reduced in different degrees compared with other mainstream algorithms, achieving a good balance between detection accuracy and model volume. The results show that the model has a more accurate detection result for the bird droppings-related defect detection of photovoltaic modules and can provide a reference for the detection of photovoltaic modules in real life.

SIIF: Semantic information interactive fusion network for photovoltaic defect segmentation

Any defects or damage on photovoltaic panels significantly reduce photoelectric conversion efficiency and service life. Therefore, the refined defect segmentation technology is the key to the quality inspection of photovoltaic equipment. However, the existing methods ignore the information interaction between multi-levels, causing semantic features to be easily lost and difficult to fuse. In this paper, we propose a segmentation network based on an interactive fusion of semantic information for solar cell defect identification, which can effectively interact with multi-level rich information and enhance the positioning capabilities of high-level semantics. Specifically, we propose a novel Multi-Level Pyramid Strategy (MLPS) to reduce the information gap between multi-level features. MLPS adopts a pooling distribution mechanism to enable multi-level features to interact effectively in the feature pyramid. In addition, to effectively integrate high-level positioning semantics into the neighbor level, we design a new Neighbor Attention Fusion module (NAF) to propagate high-level semantic information gradually. NAF learns spatial neighbor-level similarity combined with high-level attention to balance location information and contextual features. We perform comprehensive experiments to demonstrate the advancement of our approach. Our method achieves 84.3% MIOU on our solar cell dataset, outperforming existing state-of-the-art (SOTA) methods. To verify the generalization ability of our network, we train and evaluate the PSCDE dataset. The results show that our model achieves an excellent 98.35%MIOU.

Feature extraction method for ultrasonic pipeline defects based on fractional-order VMD

ABSTRACT Ensuring the safety of pipeline transportation is vital for societal well-being. Traditional methods for identifying defects in steel pipelines through ultrasonic echo signal pattern recognition often fail to extract comprehensive and effective features crucial for accurate defect detection. This study introduces an innovative feature extraction method employing a fractional Fourier transform variational modal decomposition (FRFT-VMD), hereafter referred to as the fractional-order VMD algorithm. This method utilises the fourth-order central moment and envelope entropy to optimise several key parameters: the fractional order in the Fourier transform, the number of decomposition layers, and the penalty factor in variational modal decomposition. To evaluate the effectiveness of the proposed method, ultrasonic echo signals from both finite element simulations and experimental platforms were analysed using the FRFT-VMD technique. The features extracted were then classified using a Least Squares Support Vector Machine (LSSVM) to determine defect depths. The results show a recognition accuracy of 95.2% in simulated signals and 89.1% in experimentally measured signals across various defect depths, indicating a significant improvement over existing feature extraction methodologies. The fractional-order VMD algorithm proves to be superior in extracting features that enhance the accuracy of defect identification in steel pipelines.

Study on a vehicular defect identification system for girder bottom inspection of bridges

Study on a vehicular defect identification system for girder bottom inspection of bridges

Towards a framework for reliable performance evaluation in defect prediction

Enhancing software reliability, dependability, and security requires effective identification and mitigation of defects during early development stages. Software defect prediction (SDP) models have emerged as valuable tools for this purpose. However, there is currently a lack of consensus in evaluating the predictive performance of newly proposed models, which hinders accurate measurement of progress and can lead to misleading conclusions. To tackle this challenge, we present MATTER (a fraMework towArd a consisTenT pErformance compaRison), which aims to provide reliable and consistent performance comparisons for SDP models. MATTER incorporates three key considerations. First, it establishes a global reference point, ONE (glObal baseliNe modEl), which possesses the 3S properties (Simplicity in implementation, Strong predictive ability, and Stable prediction performance), to serve as the baseline for evaluating other models. Second, it proposes using the SQA-effort-aligned threshold setting to ensure fair performance comparisons. Third, it advocates for consistent performance evaluation by adopting a set of core performance indicators that reflect the practical value of prediction models in achieving tangible progress. Through the application of MATTER to the same benchmark data sets, researchers and practitioners can obtain more accurate and meaningful insights into the performance of defect prediction models, thereby facilitating informed decision-making and improving software quality. When evaluating representative SDP models from recent years using MATTER, we surprisingly observed that: none of these models demonstrated a notable enhancement in prediction performance compared to the simple baseline model ONE. In future studies, we strongly recommend the adoption of MATTER to assess the actual usefulness of newly proposed models, promoting reliable scientific progress in defect prediction.

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

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. This study introduces an innovative filter design method that optimizes filter performance by balancing the impact of ray intensity and energy on image quality. 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. 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. The filter designed based on the parameters of Average Difference (AD) and Defect Contrast Index (DCI) can effectively improve the quality of defect images.

Using Deep Learning for PCB Fault Detection

Electronic components are mainly connected to one another using printed circuit boards, or PCBs. This phase holds significant importance in the production of electronic goods. The finished product may become unusable due to a minor PCB flaw. As a result, throughout the PCB manufacturing process, rigorous and thorough flaw identification procedures are essential. To guarantee the dependability and performance of electronic products, quality control and assurance procedures are essential in the electronics manufacturing process, especially when producing printed circuit boards (PCBs). Conventional inspection techniques are not always effective in locating different kinds of PCB flaws. Defect identification is essential to guaranteeing the dependability and functionality of electronic products since printed circuit boards, or PCBs, are essential parts of electronic gadgets. Conventional approaches to PCB defect detection, like manual inspection or traditional image processing methods, are frequently laborious, imprecise, and prone to human error. This research suggests utilizing the YOLOv5 model, a cutting-edge deep learning-based object detection technique, to create an automated PCB flaw detection system in order to overcome these difficulties