Many Types Of Defects Research Articles

Study on ultrasonic lamb wave testing technology of honeycomb sandwich structures

Abstract Honeycomb sandwich composite panels have complex structures, large sizes, many types of defects, and significant sound attenuation, which lead to low efficiency and poor sensitivity of ultrasonic non-destructive testing. In the study, a non-linear ultrasonic lamb wave testing technique for honeycomb sandwich composite panels is proposed. Firstly, the anisotropic propagation characteristics and modal structure of lamb waves in honeycomb sandwich composite panels are analyzed, and the lamb wave modes with non-linear ultrasonic accumulation are excited. Secondly, the data extraction method and imaging algorithm of non-linear ultrasonic lamb wave imaging testing are proposed. Finally, an enhanced imaging algorithm is proposed to improve image quality based on the sliding window algorithm. Based on the comparative analysis of air-coupled ultrasonic testing images and metallographic tests, it can be seen that non-linear ultrasonic lamb wave testing signals can be used to characterize the damage distribution characteristics of honeycomb sandwich composite panels, display debonding and weak debonding defects, and have the advantages of being able to perform on the same side testing and high efficiency, which can be used for non-destructive testing of honeycomb sandwich composite panels.

Investigation of performance limitations in continuous hot-dip galvanizing units associated with product defects

The goals of the work were to search and systematize the speed limits of the steel strip during hot-dip galvanizing, associated with the threat of product defects. Since speed can be combined with many other factors, this paper provides an overview of the known causes of common defects. The causes were grouped taking into account the operations of individual sections of continuous hot-dip galvanizing units. To determine the circumstances in which defects occur, a method was used that involved step-by-step stratification of retrospective data and comparison of the distribution density of influencing factors for defective and non-defective products. The method was applied in the analysis of the data on defects at the MMK Metallurgy plant in Turkey, obtained in 2020–2021. Twenty-one types of acceptable and unacceptable defects that occur during galvanizing of DX51D steel were selected for analysis. Twenty-two technological parameters were taken as factors, including the strip speed. For each selected type of the defects, a set of influencing factors is determined, and for some types of defects, the alleged causes of their occurrence are indicated. It is shown that the relationship observed for many types of defects with the strip speed can actually be caused by other factors. We determined the types of defects, the probability of which increases with an increase in the speed or the level of its change. The paper proposes measures aimed at preventing the increase in the proportion of defective products along with the performance gain.

An Active Learning Approach for Detecting Customer Induced Damages in Motherboards with Deep Neural Networks

Identifying Customer Induced Damage (CID) is a key part in warranty programs of electronics manufacturers. CID is defined as any damage in the unit performed by an unauthorized person including the customer in a Printed Circuit Board (PCB). In such cases, damaged units are not covered by warranty. The inspection of CIDs is usually performed by humans which may be costly and error prone. Modern computer vision techniques for object detection using deep neural networks can automatically and accurately detect CIDs on PCBs. The training of such networks requires a large labeled dataset of image examples of CIDs. Daily, hardware factories and repair centers generate hundreds of unlabeled images. Labeling them manually is laborious and time-consuming. Therefore, it is crucial to label the minimum amount of images such that the trained neural network can achieve comparable accuracy as if it were trained with the whole dataset. To this end, we propose an active learning approach that selects the most informative images for the object detector. For that, our approach is based on the uncertainty of the object detector, i.e., it selects new images based on class probability distribution given by the object detector. Also, we tackle some challenges that are intrinsic to this problem: i) it is a multiclass object detection problem since there are many types of defects; ii) there is a class accuracy imbalance; iii) there is a focus on recall, e.g. false positives are less harmful than false negatives, and iv) there are many images with no object which should not be selected for labeling. We evaluate this approach by using it to iteratively sample data, train and evaluate a model, and compare it with randomly sampled data. The results show that our method consistently outperforms random sampling by an average margin of 21.6%, proving to be a viable alternative for reducing the labeling cost and increasing detection accuracy in this domain.

The nature of crystallographic defects in noncrystalline tubular network block copolymers

Defects are symmetry breaking features within a crystal. They can be created during growth as well as by applied mechanical forces. We address point, line and surface defects in block copolymer crystals, concentrating on the structure of defects in the tubular network double gyroid and double diamond phases. Experimental results using 3D slice and view scanning electron microscopic tomographic imaging for many types of defects are presented and the nature of the structural details and symmetries are described. Often defects are considered detrimental to the properties and performance of the material, but if the mesoscale defects in 3D periodic block copolymers can be controlled, they can be utilized to achieve various advantageous such as new optical functionalities.

PCB Defect Detection Based on Image Processing and Improved YOLOv5

Aiming at the problems of the high cost of PCB circuit board defect detection, many types of defects, and complex shapes, this paper proposes a PCB defect detection method that combines image processing and deep learning. We use the pruned YOLOv5 algorithm and image processing method to realize the identification and location of defects on the PCB board, which improves the accuracy of defect detection. The combination algorithm first puts the original image through image enhancement methods such as straightening, denoising, sharpening, and contrast enhancement, and uses the improved YOLOv5 convolutional neural network to identify defects. The experimental results show that, without reducing the detection accuracy of the original model, the algorithm compresses the model to 2.64 MB and shortens the inference time to 20.53 ms, which greatly improves the model deployment efficiency and detection speed.

Production Quality Control as An Important Factor to Meet Buyer Quality Standards at The Garment Company PT. Sukabumi Single Estuary

The objectives of this study were achieved through the use of qualitative descriptive analysis, quantitative statistical ana-lysis, and quality control. To meet the quality target set by the Buyer, which is below 1.9%, it is necessary to know how many types of defects are found in the Finishing section and in the Final Inspection and how to reduce defects through the use of control strategies and methods applied by PT. Single Estuary. The data for this study comes from the statistics of production and damage to the Finishing department between May and August. Based on the findings of the study, it can be said that the implementation of quality control in this enterprise has been carried out quite successfully, with a noticeable improvement and a good quality improvement and is worthy of appreciation. These results show the seriousness or level of commitment of the management of PT. Muara Tunggal to continuously improve the quality according to the requests of buyers or buyers. The reduction in defects was first seen in early September 2022 with a defect rate of around 1.4%. Regarding the recommendations that can be made based on the findings, these include selecting raw materials according to Buyer's standards, hiring skilled labor, maintaining strict work ethics, providing training to employees to improve their skills, maintaining machines regularly, and improving neatness, comfort, and a safe workplace.

A generalized gradual fault diagnosis using photovoltaic plant reflectometry profile

This paper presents a generalized gradual fault diagnosis technique for photovoltaic (PV) plants using photovoltaic plant reflectometry profile (PPRP). PV plants can suffer from many types of defects; the most critical are the electrical ones that may appear either after a long or a short period of time. Many fault detection techniques are used depending on fault types, and reflectometry is one of the most efficient. Nevertheless, it has its limitations, such as location detection of the first fault only within a PV string, in addition to not being able to identify the fault type. This paper presents a mathematical model of the reflectometry technique, named hereafter the photovoltaic plant reflectometry profile (PPRP), that enhances its performance by enabling it to detect and locate multiple faults on a N-PV string, in addition to detecting any impedance mismatches, even the smallest ones, between a good PV string and one with faulty modules. Simulations were conducted to evaluate the accuracy of the PPRP analytical model considering a 7-PV string and its effectiveness to detect the expected locations of the faulty modules, and results show that the simulated reflected signal matches exactly the expected PPRP using the developed mathematical model.

Molecular Bridges Link Monolayers of Hexagonal Boron Nitride during Dielectric Breakdown

We use conduction atomic force microscopy (CAFM) to examine the soft breakdown of monocrystalline hexagonal boron nitride (h-BN) and relate the observations to the defect generation and dielectric degradation in the h-BN by charge transport simulations and density functional theory (DFT) calculations. A modified CAFM approach is adopted, whereby 500 × 500 nm2 to 3 × 3 μm2 sized metal/h-BN/metal capacitors are fabricated on 7 to 19 nm-thick h-BN crystal flakes and the CAFM tip is placed on top of the capacitor as an electrical probe. Current–voltage (I-V) sweeps and time-dependent dielectric breakdown measurements indicate that defects are generated gradually over time, leading to a progressive increase in current prior to dielectric breakdown. Typical leakage currents are around 0.3 A/cm2 at a 10 MV/cm applied field. DFT calculations indicate that many types of defects could be generated and contribute to the leakage current. However, three defects created from adjacent boron and nitrogen monovacancies exhibit the lowest formation energy. These three defects form molecular bridges between two adjacent h-BN layers, which in turn “electrically shorts” the two layers at the defect location. Electrical shorting between layers is manifested in charge transport simulations, which show that the I-V data can only be correctly modeled by incorporating a decrease in effective electrical thickness of the h-BN as well as the usual increase in trap density, which, alone, cannot explain the experimental data. An alternative breakdown mechanism, namely, the physical removal of h-BN layers under soft breakdown, appears unlikely given the h-BN is mechanically confined by the electrodes and no change in AFM topography is observed after breakdown. High-resolution transmission electron microscope micrographs of the breakdown location show a highly localized (<1 nm) breakdown path extending between the two electrodes, with the h-BN layers fractured and disrupted, but not removed.

A lightweight model for digital printing fabric defect detection based on YOLOX

Online detection of digital printing defects is a necessary but challenging topic. The performance of the current detection methods is still not ideal for the diversified patterns of digital printing fabric defects and the realtime requirements of online detection. In this paper, we proposed a lightweight model of digital printing fabric defect detection based on YOLOX. Firstly, according to the characteristics of many types of defects and complex background of digitally printed fabrics, a defect detection network structure based on YOLOX is constructed. Then, the SE attention module is introduced to enhance important features and weaken unimportant features, which make the extracted features more directional. And it can further solve the influence of small feature size on the detection accuracy of small targets. The experimental results show that the proposed model has a detection accuracy of 66.2 mAP on our self-built dataset, which is 2.7 percentage points higher than YOLOX. This method can effectively solve the problem that low detection accuracy of small defects. The proposed model can meet the real-time requirements and improve the detection accuracy of small target defects.

Multilayer Feature Extraction of AGCN on Surface Defect Detection of Steel Plates.

The development of industry is inseparable from the support of steel materials, and the modern industry has increasingly high requirements for the quality of steel plates. But the process of steel plate production produces many types of defects, such as roll marks, scratches, and scars. These defects will directly affect the quality and performance of the steel plate, so it is necessary to effectively detect them. Steel plate surface defects are characterized by their types, shape, and size: the same defect can have different morphologies, and similarities can exist between different defects. In this paper, industrial steel plate surface defect samples are analyzed, and a sample set is established by screening the collected defect images. Then, annotation and classification are performed. A multilayer feature extraction framework is developed in experiments to train a neural network on the sample set of defects. To address the problems of low automation, slow detection speed, and low accuracy of the traditional defect detection methods, the attention graph convolution network (AGCN) is investigated in this paper. Firstly, faster R-CNN is used as the basic network model for defect detection, and the visual features are jointly refined by combining attention mechanism and graph convolution neural network. The latter network enriches the contextual information in the visual features of steel plates and explores the semantic association between vision and defect types for different kinds of defects using the attention mechanism to achieve intelligent detection of defects, thus enabling our method to meet the practical needs of steel plate production.

Analysis of element loss, densification, and defects in laser-based powder-bed fusion of magnesium alloy WE43

It is well known that laser-based powder-bed fusion (L-PBF) additive manufacturing of magnesium (Mg) and its alloys is associated with high Mg loss due to vaporization (Mg Loss ) and high incidence of many types of defects in the manufactured parts/samples. Despite this, Mg Loss , densification, and defect characteristics have not been holistically considered in the determination of the optimal values of L-PBF processing parameters for Mg and its alloys. This study presents a combined modeling and experimental approach applied for a widely used Mg alloy (WE43) to address this shortcoming in the literature. First, an experimentally calibrated model is proposed to determine Mg Loss as a function of the L-PBF processing parameters. The model couples the temperature profile using a double ellipsoidal heat source with a Langmuir vaporization model and is calibrated using the width of the single-track L-PBF process and the measured Mg loss using inductively coupled plasma mass spectrometry (ICP-MS). Second, the densification of the samples is determined using a modification of the Archimedes method that considers the amount of Mg Loss in the calculation of the relative density. Third, a comprehensive and quantitative study is conducted on the relationships between the characteristics of porosity defects and the L-PBF processing parameters. Finally, the optimized L-PBF processing parameters are determined by considering the Mg Loss , densification, and the characteristics of defects. The present study yields 0.23 wt.% Mg Loss compared to 2 wt.% Mg Loss that was reported in the previous studies. Furthermore, more than 99.5% densification is achieved while only ∼2% and ∼0.5% of the total defects are characterized as keyhole and lack of fusion defects, respectively.

Improved Faster R-CNN Based Surface Defect Detection Algorithm for Plates

Defect recognition plays an important part of panel inspection, and most of the current manual inspection methods are used, but the recognition efficiency and recognition accuracy are low. The Fast-Convolutional Neural Network (Faster R-CNN) algorithm is improved, and a surface defect detection algorithm based on the improved Faster R-CNN is proposed. Firstly, the algorithm improves the bilateral filtering algorithm to smooth the image texture background. Subsequently, a feature pyramid network with a shape-variable convolutional ResNet50 network can be applied to acquire defect semantic feature maps to improve the network's ability to express the features of multiscale defects while solving the difficulty problem of many types of defects and variable shapes. To obtain more accurate defect localization information, the algorithm in this paper uses the Region of Interest Align (ROI Align) algorithm instead of the crude Region of Interest Pooling (ROI Pooling) algorithm. Then, an improved attention region recommendation network is used to improve the focus of the model on plate defects and suppress the features of complex background. Finally, a K-means algorithm is added to cluster the defect data to derive anchor frames that are better adapted to the plate defects. In this paper, a dataset containing 3216 images of surface defects of plate metal is made by acquiring surface defect images from the production site of the plate metal factory, which mainly include various defect types. This dataset is used to train and test the algorithm model of this paper, and the results of detection accuracy and detection speed are compared with those of other algorithms, which prove that the algorithm of this paper can achieve real-time detection of plate defects with high detection accuracy.

Active Thermography Diagnostics of Hidden Defects in Multilayer FR-4 Substrates

This paper discusses the applicability of pulsed and lock-in thermography using Fourier transform processing to diagnose specific types of defects in Multilayer FR-4 Substrates. Digital thermal models of test specimens with different types of defects have been created. Based on the obtained results, the methods are compared in terms of applicability and reliability in defect detection. The results show that by these methods can be detected and characterized in terms of geometric dimensions and type of certain types of specific defects arising in the production of FR-4 multilayer substrates. The presented results show that there is no obstacle to the detection of defects in different layers of the multilayer substrate within the same measurement, as well as the possibility of detecting many types of defects.

Hot-Rolled, Heavy-Rail Image Recognition Based on Deep-Learning Network

A new method for image-defect recognition is proposed that is based on a convolution network with repeated stacking of small convolution kernels and a maximum pooling layer. By improving the speed and accuracy of image-defect recognition, this new method can be applied to image recognition such as heavy-rail images with high noise and many types of defects. The experimental results showed that the new algorithm effectively improved the accuracy of heavy-rail image-defect recognition. As evidenced by the simulation study, the proposed method has a lower error rate in heavy-rail image recognition than traditional algorithms, and the method may also be applied to defect recognition of nonlinear images under strong noise conditions. Its robustness and nonlinear processing ability are impressive, and the method is featured with high theoretical depth and important application value.

Identification of wood defect using pattern recognition technique

This study proposed a classification model for timber defect classification based on an artificial neural network (ANN). Besides that, the research also focuses on determining the appropriate parameters for the neural network model in optimizing the defect identification performance, such as the number of hidden layers nodes and the number of epochs in the neural network. The neural network's performance is compared with other standard classifiers such as Naïve Bayes, K-Nearest Neighbours, and J48 Decision Tree in finding their significant differences across the multiple timber species. The classifier's performance is measured based on the F-measure due to the imbalanced dataset of the timber species. The experimental results show that the proposed classification model based on the neural network outperforms the other standard classifiers in detecting many types of defects across multiple timber species with an F-measure of 84.01%. This research demonstrates that ANN can accurately classify the defects across multiple species while defining appropriate parameters (hidden layers and epochs) for the neural network model in optimizing defect identification performance.

Sural Flap Use for the Treatment of Wounds With Underlying Osteomyelitis: Graft Size a Predictor in Outcome, a Systematic Review.

Therapeutic, Level III: Systematic review.

Construction of Visual Inspection Database for Catenary on High-speed Railways

With the rapid development of computer vision, techniques of machine vision and visual inspection have been applied into the inspection of catenary on high-speed railways. Visual inspection systems have been developed and super-high-resolution images are captured to check the status of catenary components. Automatic recognition of defects becomes very important since the number of images is too huge to be manually checked one by one. However, it is not easy for the development of recognition algorithms on catenary components. There are many types of defects to be checked on different kinds of catenary components, but the number of defect images is too small in real world. In this paper, a solution was proposed and implemented. An on-site data acquisition system was designed and developed, and different types of defects were manually made on different catenary components beforehand. Finally, a visual inspection database was successfully constructed, including plenty of different kinds of catenary components, different types of defects, in different inspection conditions. The visual inspection database will be of great use in the development and test of recognition algorithms for catenary.

Research on the defect types judgment in wind turbine blades using ultrasonic NDT

Beacuse of the complex internal structure of wind turbine blades, there are many types of defects. There will be important significance to determine the type of defects to assess the blade residual life and feedback on the design and production process. At present the type of defects found by ultrasonic nondestructive testing (NDT) can't be determined. Physical models of several typical defects are constructed. The sound intensity reflection coefficients of different physical models are calculated seperately, and the calculation method of the sound intensity reflection coefficient of actual defect is presented. Combined the calculated coefficients with the defect information tested, a method of recognize the type of defects is presented. Actual specimen testing is used to verify the effectiveness of the method.

The Detailed Description of Residential Defects in Years 2010 – 2011 of &lt;i&gt;Citra &lt;/i&gt;Garden Residence in Indonesia

The detailed description of residential defects in years 2010 – 2011 of Citra Garden Residence in Indonesia was studied. Defect data were collected from interviews that have been carried out to the field supervisor and to the customer service in Citra Garden Residence. The defects observed were mainly on leakage that comes from the tile, natural stone walls, concrete roof, while the defects in addition to leakage is the wall, the painting, the floor and the ceramic wall, the plafond, the stairs, the roof, wood window &amp; door frame, aluminum window &amp; door frame, fence door &amp; garage, wood stone, railing/grill, the floor and the ceramic wall spotted and sanitary. The data were processed and analyzed by using SPSS (Statistical Program for Social Science) software program to obtain statistical overview of the many types of defects for each category (minor, moderate, and serious) and the timing of the defects (Before Hand Over, During Hand-Over and After Hand-Over). The results showed that for the year 2010, the occurrence of defects in Citra Garden Residence were of moderate non-structural defects = 40.95%, of moderate structural defects = 23.334%, of minor non-structural defects = 21.904%, and of minor structural defect = 13.809%. There were neither serious structural defects nor serious non-structural defects occurred. The defects for the year 2011 were of moderate non-structural defects = 37.582%, of minor non-structural defects = 26.813%, of moderate structural defects = 21.538%, and of minor structural defects = 14.067%. Neither serious structural defects nor serious non-structural defects were found.

Interval Dependant Thresholding based De-noising of Ultrasonic TOFD Signals from Austenitic Stainless Steel Welds

Austenitic stainless steel has structural values in almost all industries. It is one of the most widely used materials. Qualitative assessment of such important components is of greater importance in very sensitive applications such as nuclear reactor vessels. Ultrasonic based Time of Flight diffraction is a reliable technique in testing the materials for many types of defects in welds. Echo signals obtained by the receiver are also accompanied by ambient scattering noise due to the signal interaction with the grains of the material. This noise degrades the quality of the defect echo signal and at times completely deteriorates the shape of the defect signal there by making it unsuitable for characterization. Signal processing is a necessary aspect in restoring the defect signal‟s shape, size etc for proper detection and positioning of the defect in the material. Wavelet Transform is one such popular technique for denoising of the signals in which thresholding of high frequency components removes the unwanted noise. Conventional global thresholding gives good improvement in SNR values. This paper implements an Interval dependant thresholding method and it is found that it has very good improvement in SNR values compared to conventional techniques.