Defect Detection Research Articles

Machine learning for advancing laser powder bed fusion of stainless steel

In the dynamic landscape of advanced manufacturing, the confluence of laser powder bed fusion (LPBF) and machine learning (ML) has recently garnered significant attention in many applications. This review investigates the confluence of LPBF and ML, specifically within the specific domain of stainless steel. Firstly, it delves into LPBF principles, including an overview of critical process parameters and associated defects. Secondly, the paper meticulously addresses the distinct challenges posed by steel in additive manufacturing (AM), highlighting factors such as chemical composition, anisotropic microstructure, and oxide film formation, all of which require specialized considerations. Thirdly, the spotlight shifts to the pivotal role of ML, covering predictive modeling for process parameters, real-time defect detection, and quality control. This paper highlights recent advances, revealing how data-driven approaches can accelerate process understanding and part qualification. Eventually, this review offers insights into the future integration of ML in LPBF for steel, providing valuable perspectives on potential advancements in the field of AM.

Transmission line defect detection based on feature fusion

Transmission lines are the lifeblood of the power system, and regular line inspections are required to ensure the operation of transmission lines. However, due to the special high-voltage environment of transmission lines, the pictures collected by UAVs are characterized by many background interferences and small defect samples, and it is difficult to directly detect line inspection methods based on deep learning. For this reason, this paper proposes a high and low-dimensional fusion framework, which divides the target detection task into four modules, and greatly reduces the technical volume of the model while retaining the high-dimensional features through the collaboration between modules. This paper also introduces the window attention mechanism based on YOLOv7, which allows the model to focus on local information and improve the model’s detection ability on small targets and also introduces the Wise-IOU loss to improve the model’s prediction accuracy and inference time. Experiments prove that the method in this paper can significantly improve the accuracy of model prediction while meeting the speed requirements of industrial scenarios.

A novel method for multiple targets localization based on normalized cross-correlation adaptive variable step-size dynamic template matching

The template matching method has been widely utilized in the defect detection of wafer surfaces. However, the traditional matching approaches are limited by illumination, noise, and deformation, which cannot meet the requirements of accuracy and robustness. In this paper, a novel multiple targets localization method, named Normalized Cross-correlation Adaptive Variable Step-Size Dynamic Template (NCC-AVSSDT) matching, is proposed to improve the accuracy and efficiency of image localization, which combines the advantages of NCC and AVSSDT. The AVSSDT method is utilized to dynamically adjust the scanning step size based on the NCC matching coefficients. This approach optimizes the scanning process, accelerating convergence toward the optimal matching position. Experimental results verify the accuracy and robustness of the proposed method under different conditions, especially when dealing with rotational variations and variations in noise textures. Therefore, NCC-AVSSDT can be used to perform multiple targets localization of chip image in nearly real-time. Three experiment types were used for comprehensive evaluations, including multiple targets, noise, and rotation angles. Experimental results show that NCC-AVSSDT is much better than the sequential similarity detection algorithm and mean absolute deviation methods in terms of multiple targets (0.667 vs 0.811 s, 0.832 s) and success rate (100% vs 35%, 20%).

Steel strip surface defect detection based on multiscale feature sensing and adaptive feature fusion

A surface defect detection method for hot-rolled steel strips was proposed to address the challenges of detecting small target defects, significant differences in morphology, and unclear defect characteristics. This method is based on multiscale feature perception and adaptive feature fusion. First, based on the spatial distribution characteristics of the steel strip image, redundant background interference is removed using automatic gamma correction and Otsu thresholding. Second, based on the characteristics of surface defects in steel strips, this paper proposes TDB-YOLO (YOLO with a small target detection layer), a Bidirectional Feature Pyramid Network (BiFPN), and Double Cross Stage Partial (CSP) Bottleneck with three convolutions (DC3). To detect small object defects, a small target detection layer with a smaller receptive field focuses on fine-grained features, reducing the model’s probability of missed detection. In terms of feature extraction, DC3 enhances the interaction of feature information from different spatial scales, enabling the model to effectively handle features of varying scales. In terms of feature fusion, the BiFPN is used to adaptively fuse deep-level and shallow-level feature information, enhancing the semantic richness of the feature information. Ultimately, the proposed model in this paper achieved an accuracy of 90.3% and a recall rate of 88.0% for surface defects in steel strips. The mean average precision was 90.4%, and the frames per second was 33. The detection performance of this model outperformed those of other detection models, demonstrating its ability to effectively meet the real-time detection requirements of surface defects in industrial scenarios on steel strips.

MSC-AD: A Multiscene Unsupervised Anomaly Detection Dataset for Small Defect Detection of Casting Surface

MSC-AD: A Multiscene Unsupervised Anomaly Detection Dataset for Small Defect Detection of Casting Surface

Automated ultrasonic testing for near-surface flaws in CFRP

ABSTRACT In ultrasonic testing of Carbon Fiber Reinforced Polymers (CFRP), signals of near-surface flaws are often submerged in interface signals, resulting in blind spots for defect detection. To address this issue, this paper presents an autocorrelation imaging algorithm that combines Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) with l 0 -norm sparse representation. Firstly, the ultrasonic signal can be modelled as a convolution process. By using MOMEDA, the sequence of reflected pulses in the ultrasonic signal is obtained. Then, the l 0 -norm sparse representation is utilised to enhance the temporal resolution of the sequence, successfully separating near-surface defect signals from interface signals. Finally, the processed data is input into the autocorrelation algorithm, achieving automated imaging of near-surface flaws. Simulation results demonstrate the efficacy of the algorithm in separating overlapping signals. Finally, experimental validation was conducted on flaws at three different depths. The algorithm presented in this paper is capable of identifying all near-surface flaws, with a defect size error rate consistently below 5%.

A Novel Hybrid YOLO Approach for Precise Paper Defect Detection With a Dual-Layer Template and an Attention Mechanism

A Novel Hybrid YOLO Approach for Precise Paper Defect Detection With a Dual-Layer Template and an Attention Mechanism

Non-Destructive Testing (NDT) Method for Defect Detection in Glass Fibre Reinforced Plastic/Polymer (GFRP/GRP) Composite Materials Structures: A Review

Various industries are considering metal product in lieu composite materials. Structural engineers appreciate glass fibre reinforced plastic/polymer (GFRP/GRP) because to its high modulus of elasticity, ratio of strength to weight, and corrosion resistance features. It has been discovered that structural engineering defects to be leading cause for the catastrophic consequences. This paper provides an overview of available NDT methods that can be employed to assess the quality of GFRP/GRP composite materials. The most common NDT method used by researchers and practitioners is also discussed, along with the advantages, disadvantages, characteristics, and potential applications of these materials. The review will lead the research using ultrasonic testing as a potential method employing low-frequency transducers with multielement architecture. This study will lead industry players, GFRP/GRP manufacturers, researchers, and NDT practitioners towards the development of technical standards for ultrasonic testing on GFRP/GRP in Malaysia.

Comparative analysis of approaches necessary for designing a robotic module for industrial parts defect detection

Over the past two decades, the most frequently implemented method of oil extraction in the Russian Federation has been a mechanized method using an electric submersible pump (ESP) unit. Due to the remoteness of the fields from the base of repair enterprises, the cost of transporting spent but maintainable ESPs significantly exceeds their purchase price, which over time leads to the formation of a significant amount of decommissioned oil production equipment. To solve this problem, it is proposed to develop a mobile robotic module for sorting, defecting and storing pump parts, thanks to its use, minor repairs of equipment will be possible directly at production sites. The article deals with the problems of complex flaw detection of metal and non-metallic parts with the possibility of applying the developed methodology for a wide range of industrial products, at the same time, the main attention is paid to work with ESP parts. Based on the decomposition of the problem, the most problematic operations were identified: classification of parts, surface control (identification of defects), dimensional control. The results of a brief comparative analysis for each of the above subtasks are based on a review of the scientific literature over the past 30 years, with the largest number of sources reviewed in the last 5 years. As a result optimal methods for solving the task were derived — a machine learning technique for classifying surface defects, the use of a coordinate measuring machine with a manipulator for dimensional control. A new approach is also proposed to solve the main problem of machine learning methods (lack of training samples) in the form of using synthetic photorealistic images for classification with transfer of defect features from semantically close and publicly available training samples.

Printed Circuit Board Defect Detection Algorithm Based on yolov5s Structure Improvement

In terms of the existing PCB defect detection algorithm in complicated model structure with huge numbers of parameters slow speed, low detection accuracy and other problems, an improved PCB defect detection algorithm which is based upon yolov5s is proposed. Taking the yolov5s model as the main framework, the Omni-Dimensional Dynamic Convolution (OdConv) structure is used to replace the backbone of the original model, and the Convolutional Block Attention Module (CBAM) attention mechanism is introduced after the Conv layer of the Neck network. CBAM attention mechanism is brought in after the Conv layer of the Neck network (Neck), and the Focal-EIoU Loss function is modified by the Complete Intersection and Merger Ratio Loss (CIoU Loss) function, and different weights are assigned to high-quality samples and low-quality samples respectively, which solves sample imbalance existing in the target detection problem, and the results show that this algorithm is comparable to the original model. Experiments are conducted and the results show that this paper’s algorithm improves the mAP by 1.9%, speeds up by 47.75Fps, decrease the model size by 13.26 M, and reduces the computation volume by 12.3 G compared to yolov5s at IoU = 0.5.

WARM: A wavelet adaptive restoration module for surface anomaly detection

As a key component of lithium-ion batteries, surface defect detection of pole-piece is vital for quality control. Detection methods based on supervised have been used in pole-piece. However, the long cycle for data collection cannot meet the needs for fast construction and application of production line, making self-supervised a promising alternative. Although existing restoration-based methods have been widely studied, it’s still difficult to balance the problem of restoring normal regions and distinguishing abnormalities. We propose a Wavelet Adaptive Reconstruction Module by exploiting the obvious spatial and frequency differences between intra-class normal and abnormal features. Through training, the wavelet coefficients of the inputs are adaptively refined, potently obstructing the identity map from input image to restored image, thus improving the generalization of restoration network. The structure only consists of 2 simple Unets and WARM, it solves the anomaly detection task for pole-piece, and also outperforms other state-of-the-arts on MvTec AD.

Automatic flaw detection in sectoral scans using machine learning

Phased array ultrasonic testing (PAUT) requires highly trained and qualified personnel to interpret and analyze images. It takes a solid understanding of wave propagation physics to comprehend the generated images. As such, the inspector's judgment and level of experience have a significant impact on the analysis's outcome. In addition, the procedure is prone to error and laborious. AI had shown to be effective in computer vision in a variety of classification and detection tasks. Regarding PAUT, studies have also demonstrated that machine learning may be able to identify defects with a level of accuracy that is on par or even superior to that of trained and qualified inspectors. Nonetheless, the use of computer vision in PAUT remains very limited. The primary cause of this is the challenge accessing large databases of labelled inspections. In fact, a considerable amount of training data is required for machine learning. While it is easy to access sizeable, labelled databases of MRI scans or photographs for instance, that is not the case in PAUT because inspection results are usually confidential. In this project, a large database was generated using mock-ups commonly used to train and evaluate inspectors. The different defects contained in these mock-ups were used to train a machine learning model. The data was acquired with several different probes centered at different frequencies. Each acquisition was performed using Full Matrix Capture (FMC). The post-processing of the data contained in the FMC allows to compute any sectoral scan from its focal laws. As a result, a comprehensive database composed of hundreds of thousands of sectoral scans was generated from these few FMC acquisitions. The completeness of this database facilitated robust training of a defect detection model for PAUT sectoral scans. The evaluation of the model demonstrated its ability to generalize even to defect types it had never been trained on. Furthermore, the detection performance remained consistent even in high noise conditions where the Contrast-to-Noise Ratio (CNR) was very low.

Stability and reliability analysis for broadband current array sensing system applied to GIS

Abstract In order to further improve the defect detection capability of gas insulated switchgear and avoid equipment faults, broadband current array sensing system is designed to simultaneously monitor the system status such as partial discharge, relative dielectric loss, and power frequency overvoltage. The dynamic model of the system is analysed, and based on which, the system state equation is obtained meanwhile fault observer is designed, and the system stability criterion under H ∞ condition is calculated. The reliability of the system under different packet loss rates is verified based on cloud models, and the principle of using appropriate electromagnetic compatibility to prevent packet loss rates below 50% is proposed.

Self-Reporting Microsensors Inspired by Noctiluca Scintillans for Small-Defect Positioning and Electrical-Stress Visualization in Polymers.

Small defects induce concentrated electrical stress in dielectric polymers, leading to premature failure of materials. Existing sensing methods fail to effectively visualize these defects owing to the invisible-energy state of the electric field. Thus, it is necessary to establish a nondestructive method for the real-time detection of small defects in dielectric polymers. In this study, a self-reporting microsensor (SRM) inspired by Noctiluca scintillans is designed to endow materials with the ability of self-detection for defects and electrical stress. The SRM leverages the energy of a nearby electric field to emit measurable fluorescence, enabling defect localization and diagnosis as well as electrical-stress visualization. A controllable dielectric microsphere is constructed to achieve an adjustable electroluminescence threshold for the SRM, thereby increasing its detection accuracy while decreasing the electroluminescence threshold. The potential degradation in the polymer performance owing to SRM implantation is addressed by assembling long molecular chains on the SRM surface to spontaneously generate an interpenetrating network. Results of finite element analyses and experiments demonstrate that the SRM can effectively realize nondestructive visualization and positioning of small defects and concentrated electrical stress in polymers, positioning it as a promising sensing method for monitoring the electric field and charge distribution in materials.

YOLOv5s fabric defect detection model with mixed attention mechanism

In the field of industrial quality inspection, fabric defect detection is regarded as an important research topic. The traditional inspection method mainly relies on manual visual inspection, which is time-consuming and easy to be affected by human factors, and has the problems of low accuracy, slow speed and high cost. Therefore, it is very important to use digital image processing technology and target detection technology to develop high precision, high efficiency and low-cost fabric defect detection model suitable for application deployment. Driven by deep learning-based neural network technology, many models with excellent performance have emerged in the field of object detection. This paper will improve on the basis of YOLOv5s model. ACmix module is introduced in Backbone layer of YOLOv5s model. This module overcomes the problem that convolution operation may cause local information loss after feature extraction, improves the model's limitation on feature extraction of complex background images, and enhances the model's processing ability in local feature fusion. Compared with the original YOLOv5s algorithm, mAP has improved by 0.6%. In the complex background, the detection accuracy of fabric defects targets has been significantly optimized and improved.

Image-based automatic multiple-defect detection of urban utility tunnel using UUTNet

In-service urban utility tunnels (UUT) suffer from cracks, corrosion, and leakage defects, which rises the chance of major accidents. However, prevailing detection methods for UUT remain reliant on manual inspection and subjective judgment, or traditional image processing technologies, such methods may not be able to obtain accurate defect information. This study proposes a novel and effective network called UUTNet based on the constructed UUT dataset for defects detection. Considering that the UUT defects has a certain distribution correlation, the attention module is introduced to the Pyramid Scene Parsing Network to capture the relation. By adding the hybrid dilated convolution after the feature extraction layer, the receptive field is expanded to further extract global and local features. The performance of UUTNet was evaluated based on the metrics MIoU, F1-score, Accuracy, and robustness. Comparative experiments were conducted, and the results showed the UUTNet achieved the best detection performance, achieving 0.7615 MIoU, 0.9806 Accuracy and 0.8012 F1-score. The MIoU was further improved to 0.7847 by utilizing the Bayesian optimization. Three extreme inspection scenes, including uneven illumination, high brightness, and obstacle interference, were applied to validate model robustness. The proposed method offers robust technical assistance for detecting defects in the UUT and precisely assessing the distribution and extent of these defects.

Detection of non-cardiac fetal abnormalities by ultrasound at 11-14 weeks: systematic review and meta-analysis.

To assess diagnostic accuracy of 2D ultrasound at 11-14 weeks gestation as a screening test for individual fetal anomalies and identify screening factors impacting detection. Systematic review and meta-analysis, developed and registered with PROSPERO (CRD42018111781). MEDLINE, EMBASE, Web of Science Core Collection and The Cochrane Library) were searched for studies evaluating the diagnostic accuracy of screening for 16 pre-specified, non-cardiac, congenital anomalies considered to be of interest to the early anomaly scan. We included prospective and retrospective studies from any healthcare setting and low risk, mixed risk and unselected populations. The reference standard was the detection of an anomaly on postnatal or post-mortem examination. Data were extracted to populate 2 x 2 tables and meta-analysis (random-effects model) undertaken to determine the diagnostic accuracy of screening for the pre-specified anomalies (individually and as a composite). Secondary analyses were performed to determine the impact of (1) imaging protocol (2) ultrasound modality (3) publication year and (4) index of sonographer suspicion at time of scan. Post-hoc secondary analysis was conducted to assess performance for studies from 2010. Risk of bias and quality assessment was undertaken for included studies using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). From 5684 citations, 202 papers were identified as eligible and reviewed, resulting in the inclusion of 526,322 fetuses (52 studies) of which 2,399 were affected by one or more of the 16 anomalies. Individual anomalies were not equally amenable to detection on first trimester ultrasound ranging from high (>80%) detection rates for severe conditions including acrania (98%), gastroschisis (96%) and exomphalos (95%) and holoprosencephaly (88%); they were lower for open spina bifida (69%), lower urinary tract obstruction (66%) lethal skeletal dysplasias (57%) and limb reduction defects (50%) and below 50% for facial clefts (43%), polydactyly (40%) and congenital diaphragmatic hernia (38). Conditions with low (<30%) detection rates included bilateral renal agenesis (25%), closed spina bifida (21%), isolated cleft lip only (14%) and talipes (11%). Specificity was >99% for all anomalies. Secondary analysis showed improvement of detection with publication year, and that the use of imaging protocols had a statistically significant impact on screening performance (p<0.0001). Accurate detection of congenital anomalies using first trimester ultrasound is feasible. In this study we have determined screening characteristics for individual anomalies and have shown that detection rates and false positive rates are dependent on the type of anomaly. The use of a standardised protocol allows diagnostic performance to be maximised, and this particularly enhances screening performance for the detection of spina bifida, facial clefts and limb reduction defects. Highlighting the types of anomalies amenable to diagnosis and determining favourable screening test factors can support the development of first-trimester anomaly screening programs. This article is protected by copyright. All rights reserved.

Interface debonding defect detection for CFST columns based on EMI measurements: Experiment, numerical simulation and blind inspection in practice

Concrete-filled steel tube (CFST) members have been widely used in skyscrapers and long-span bridges. Non-uniformly distributed hydration heat during curing, inadequate compaction during construction and unavoidable shrinkage and creep of mass concrete in service most likely lead to interface debonding defects between concrete core and steel tube, which has been a common concern. The development of effective non-destructive inspection methods for interface bonding condition of existing CFST members is critical. In this study, an interface debonding defect detection method based on electromechanical impedance (EMI) measurement using surface-mounted piezoelectric-lead-zirconate-titanate (PZT) sensors is proposed at first. Then, experimental study on the feasibility of the proposed approach is carried out with scaled CFST specimens with artificially mimicked interface debonding defects. EMI of surface-mounted PZT sensors at different frequency bands are measured and compared to verify the detectability of the proposed approach for the mimicked interface debonding defects. Thirdly, a multi-physics CFST-PZT coupling finite element model (FEM) with interface debonding defects is established, and EMI of surface-mounted PZT sensors at different locations is simulated. The influence of interface debonding defects on EMI measurements is illustrated. Finally, a blind inspection on the interface bonding condition of selected CFST columns in an existing skyscraper in service using the proposed approach is carried out. Both bonding and debonding defect in the tested CFST columns are detected correctly and validated with drilling hole observation. Experimental, numerical and engineering application results show the proposed approach is effective for interface debonding detection of CFST members and sensitive to minor debonding defect of 0.3 mm in CFST members.

PCB defect detection algorithm based on CDI-YOLO

During the manufacturing process of printed circuit boards (PCBs), quality defects can occur, which can affect the performance and reliability of PCBs. Existing deep learning-based PCB defect detection methods are difficult to simultaneously achieve the goals of high detection accuracy, fast detection speed, and small number of parameters. Therefore, this paper proposes a PCB defect detection algorithm based on CDI-YOLO. Firstly, the coordinate attention mechanism (CA) is introduced to improve the backbone and neck network of YOLOv7-tiny, enhance the feature extraction capability of the model, and thus improve the accuracy of model detection. Secondly, DSConv is used to replace part of the common convolution in YOLOv7-tiny to achieve lower computing costs and faster detection speed. Finally, Inner-CIoU is used as the bounding box regression loss function of CDI-YOLO to speed up the bounding box regression process. The experimental results show that the method achieves 98.3% mAP on the PCB defect dataset, the detection speed is 128 frames per second (FPS), the parameters is 5.8 M, and the giga floating-point operations per second (GFLOPs) is 12.6 G. Compared with the existing methods, the comprehensive performance of this method has advantages.

Defects Detection of Lithium-Ion Battery Electrode Coatings Based on Background Reconstruction and Improved Canny Algorithm

Aiming to address the problems of uneven brightness and small defects of low contrast on the surface of lithium-ion battery electrode (LIBE) coatings, this study proposes a defect detection method that combines background reconstruction with an enhanced Canny algorithm. Firstly, we acquire and pre-process the electrode coating image, considering the characteristics of the electrode coating process and defects. Secondly, background reconstruction and the difference method are introduced to achieve the rough localization of coating defects. Furthermore, the image with potential defects undergoes enhancement through improved Gamma correction, and the PSO-OTSU algorithm with adaptive searching is applied to determine the optimal segmentation. Finally, precise defect detection is accomplished using the improved Canny algorithm and morphological processing. The experimental results show that, compared with the maximum entropy method, the region growth method, and the traditional Canny algorithm, the algorithm in this paper has a higher segmentation accuracy for defects. It better retains defect edge features and provides a more accurate detection effect for defects like scratches, dark spots, bright spots, metal leakage, and decarburization, which are difficult to recognize on the background of coating areas of electrodes. The proposed method is suitable for the online real-time defect detection of LIBE coating defects in actual lithium-ion battery industrial production.