Pin Defects Research Articles

Rapid detection technology of sewing thread based on DeblurGAN-BSV3 defuzzification algorithm and ST-FPN detection algorithm

Stitch defect detection is an important means to ensure the quality of stitched fabrics. In order to realize the intellectualization of the sewing process, a rapid defect detection method based on machine vision was proposed. In order to solve the problem of motion blur caused by sewing speed, an image DeblurGAN-BSV3 algorithm is proposed. Firstly, different attention fusion mechanisms are introduced to achieve accurate extraction of image features. Secondly, the depth-separable convolution of the trunk network is replaced by blueprint convolution to ensure the processing speed of the detection algorithm and the clarity of the output image. In order to solve the difficulty of rapid detection of pin defects, a student–teacher feature pyramid matching method was proposed. Firstly, the abnormal region interpretation technology was used to explicitly visualize the defect area of the identified abnormal instances, and then the abnormal detection score was performed on the detected images to make a quality judgment. The experimental results show that the combination of the above two methods has a good ability for rapid defect detection. Rapid detection of common pin defects is realized.

Retraction Note: Capacitance pin defect detection based on deep learning

Retraction Note: Capacitance pin defect detection based on deep learning

Structuring step dependent characteristics in joining using pin-like structures in the vibration welding process

Abstract With this study, correlations in the structuring step of pin-like joining were derived. Increased friction energy due to higher amplitude or force leads to a reduction in structuring time. Changes in thermo-mechanical properties for humid specimens result in increased process times. The theoretical geometry of the pin-like structures is well reproduced in the lower pin area, regardless of the process control. In the upper pin area, increased force and amplitude results in increased defects and air inclusions as a result of an accelerate and more inhomogeneous pin formation. Humidity does not affect the general pin geometry, but should be avoided due to increased air inclusions that can weaken the structure. For the multi-material joints, high bond strengths of up to 30 % of the base material (max. 50 % possible with the geometry used) can be achieved. Therefore, a minimum undercut is required. Once this is reached, the pin defects and the corresponding pin-foot ratio are decisive for the resulting bond quality.

Structural design and simulation study of intelligent defect elimination equipment for high‐voltage transmission line pin defects

AbstractTransmission lines may suffer from pin defects, necessitating regular patrols and maintenance. The traditional maintenance approach not only involves a substantial workload and low efficiency but also poses a threat to the safety of maintenance personnel. To address the issue of pin defects in high‐voltage transmission lines, this paper introduces the design of an intelligent pin‐eliminating robot. The research focuses on two main aspects: firstly, analyzing the transmission line environment and employing Solidworks software to design the mechanical structure of the robot. Subsequently, a static stress analysis of the robot's structure is conducted to ensure the rationality of the design. The second aspect addresses the impact of the robot on the electric field distribution of the transmission line in high electric field intensity areas and whether the robot itself experiences discharge phenomena. COMSOL simulation software is utilized to globally calculate and analyze the electric field of the robot. Research results indicate that the threshold for the curvature radius of the pin‐eliminating robot's tip is 2.5mm, and values below this threshold may lead to the occurrence of discharge phenomena. The optimized pin‐eliminating robot, under normal operation, does not exhibit discharge phenomena in a 500kV strong electric field.

Structure formation in pin-like joining using vibration welding technology

Joining technologies have a decisive role in the product development cycle. Welding processes are widely used industrially, which, in addition to a number of advantages, also have limitations on possible material combinations. Therefore, novel joining processes are necessary, like the joining using pin-like structures using the vibration welding technology. Beside known material and process correlations, the structure formation effect the resulting bond properties. For this reason, the structuring step was analyzed within this study. The investigations allowed a model-based description of the formation of the pin-like structures to be derived. In addition, the investigations show that an additional energy input during pin development prevents a pin defect during tool pull removement due to a reduced weld line. This can have particular advantages for material combinations with a bonding-partner of high stiffness.

Identification of intrinsic defects and hydrogen passivation in InP using hybrid functional

Indium phosphide is widely used in electronics and photovoltaic devices due to its high electro-optical conversion efficiency, high electron mobility, and good radiation resistance. Defects are the main limitation for the performance of InP devices. In this work, based on hybrid functional with finite size correction, electronic properties of intrinsic and H-related defects have been investigated in InP. We found that PIn defect is the most stable intrinsic defect with the lowest formation energy. Defect signals detected experimentally are defined by our calculated results. Experimentally observed electron traps with the energy level of EC − 0.66 eV and EC − 0.68 eV are ascribed to the transition level ɛ(−1/−2) and ɛ(−2/−3) of In vacancies. The hydrogenated vacancies in InP have been systematically reported in the present work. Formation energies of H-related defects indicate that hydrogen atoms prefer to bind to In vacancy than P vacancy. The formation energy of In vacancy decreases with the addition of H, while that of P vacancy increases. For hydrogenated In vacancies, it captures fewer electrons than bare In vacancies when the Fermi level is close to CBM. Especially for the VIn − 3H structure, it is 0 charge state in all Fermi levels so that it will not tend to capture electron or hole. Our work is helpful to explain experimental phenomena and radiation-induced damages and improve the performance of InP devices.

Detection of the Pin Defects of Power Transmission Lines Based on Improved TPH-MobileNetv3

Pins are essential connecting components in power transmission lines. Their extensive use yet leads to frequent defects. Given the small size of a pin and many similar components, the detection of such defects is not ideal, which is a technological problem in the identification and diagnosis of power defects. In response to the large size, complex background, and on-site requirements, such as real-time detection, of power transmission lines, this paper proposes a method to detect pin defects based on TPH-MobileNetv3 (Transformer prediction Head Mobilenetv3). This paper modifies and adds a self-attention layer to MobilNetV3-Small to improve the feature extraction capability of small targets after downsampling. A feature fusion structure with layers of self-attention and a convolutional block attention module (CBAM) is added to the neck network, and a transformer prediction head are added to the head network so that different scale characteristics can be fused and focused from space and channels to strengthen the detection of small targets. Compared with the traditional MobileNetV3, the detection accuracy of the algorithm in this paper has been raised by 24%, as shown in the detection results of measured data. Moreover, compared with the mainstream algorithms with the same detection accuracy, this algorithm not only reduces the model size and significantly enhances detection efficiency but also satisfies the requirement of edge image processing of power inspection.

Research Progress on Dynamics of Skyrmions Under the Effect of Pinning

Magnetic skyrmions offer the potential as an information carrier for the next-generation storage and computing devices such as racetrack memory devices, logic gates, and even neurocomputing owing to the topology-protected structure, nano-scale size and relatively low driving current compared to traditional devices. The discovery of magnetic skyrmions at room temperature also lays the foundation for realizing room-temperature magnetic skyrmion spintronic devices. Defects and impurities are unavoidable in real materials. These pinning centers significantly affect the dynamics of the skyrmion, including the critical driving current, and the Hall angle, etc. The study on room-temperature magnetic skyrmions in thin films has shown that the effect of pinning can be dominant at room temperature. Hence, the study of pinning effects and the skyrmion-pinning interaction is significant to the motion of magnetic skyrmions at room temperature and the realization of room-temperature magnetic skyrmion spin devices Magnetic skyrmion motion can be strongly modified under the effect of pinning (defects and purities). Moreover, these effects can inspire us to utilize artificial pinning centers to manipulate the dynamics of skyrmions. In this article, we introduce the models for skyrmion motion and pinning effect at room temperature and gives an overview of the methods for creating different types of pinning and their impact on the skyrmions. When pinning is induced by replacing or adding atoms, setting vacancies, changing the material thickness or curvature, or changing magnetic parameters, the skyrmions hall angle will vary. Moreover, the magnetic skyrmions can be fixed in a particular region, move along a specific orbit and overcome thermal perturbations at room temperature under the effect of pinning, which helps to realize magnetic skyrmion spin devices at room temperature.

Origins of high–performance giant dielectric properties in TiO2 co-doped with aliovalent ions via broadband dielectric spectroscopy

(Zn1/3Ta2/3)xTi1-xO2 ceramics with x = 0.01 (ZnTTO-1 %) and x = 0.1 (ZnTTO-10 %) were prepared by a solid-state reaction method. Remarkably, the ZnTTO-1 % and ZnTTO-10 % ceramics sintered at 1500 °C for 2 h showed a high dielectric permittivity of approximately 3.90 × 104 and 6.23 × 104 at 1 kHz with low tanδ values of 0.014 and 0.024, respectively. Furthermore, the ZnTTO-1 % ceramic exhibited good temperature stability with a temperature coefficient of <±15 % in the temperature range of 218–473 K (−55–200 °C), which meets the requirement of the X9R capacitor application. The dielectric relaxations in the wide temperature range of 15–483 K was attained using broadband dielectric spectroscopy to clarify the sources of the excellent dielectric properties. The best performance was demonstrated in the ZnTTO-1 % ceramic. The electron pin defect dipole mechanism, electron hopping, and interface effects are attributed to the giant dielectric responses.

Defect Detection Scheme of Pins for Aviation Connectors Based on Image Segmentation and Improved RESNET-50

In this paper, a new detection method of pin defects based on image segmentation and ResNe-50 is proposed, which realizes the defect detection of faulty pins in many aviation connectors. In this paper, a new dataset image segmentation method is used to segment many aviation connectors in a single image to generate a dataset, which reduces the tedious work of manually labeling the dataset. In the defect detection model, based on ResNet-50, a ResNet-B residual structure is introduced to reduce the loss of features during information extraction; a continuously differentiable CELU is used as the activation function to reduce the neuron death problem of ReLU; a new deformable convolution network (DCN v2) is introduced as the convolution kernel structure of the model to improve the recognition of aviation connectors with prominent geometric deformation pin recognition. The improved model achieved 97.2% and 94.4% accuracy for skewed and missing pins, respectively, in the experiments. The detection accuracy improved by 1.91% to 96.62% compared to the conventional ResNet-50. Compared with the traditional model, the improved model has better generalization ability.

RETRACTED ARTICLE: Capacitance pin defect detection based on deep learning

RETRACTED ARTICLE: Capacitance pin defect detection based on deep learning

Automatic Detection of Chip Pin Defect in Semiconductor Assembly Using Vision Measurement

Abstract With the development of semiconductor assembly technology, the continuous requirement for the improvement of chip quality caused an increasing pressure on the assembly manufacturing process. The defects of chip pin had been mostly verified by manual inspection, which has low efficiency, high cost, and low reliability. In this paper, we propose a vision measurement method to detect the chip pin defects, such as the pin warping and collapse that heavily influence the quality of chip assembly. This task is performed by extracting the corner feature of the chip pins, computing the corresponding point pairs in the binocular sequence images, and reconstructing the target features of the chip. In the corner feature step, the corner detection of the pins using the gradient correlation matrices (GCM), and the feature point extraction of the chip package body surface using the crossing points of the fitting lines are introduced, respectively. After obtaining the corresponding point pairs, the feature points are utilized to reconstruct the three dimensional (3D) coordinate information in the binocular vision measurement system, and the key geometry dimension of the pins is computed, which reflects whether the quality of the chip pins is up to the standard. The proposed method is evaluated on the chip data, and the effectiveness is also verified by the comparison experiments.

A NOVEL SAMPLE LABELLING CRITERION FOR PIN DEFECT DETECTION IN UAV IMAGE

Abstract. With the rapid development of UAV technology, defect detection based on UAV images has expanded from power components such as insulators and dampers to bolts and pins. Different from the defect detection of insulators or dampers, there are two main difficulties in pin defect detection: (1) It is very small for bolts and pins compared to the entire image, usually less than 1%, and there are not enough features for detection; (2) Only bolts on link fittings need to be fixed with pins, while bolts in other parts do not need, so it is difficult to judge whether there are pin defects on bolts only based on the absence of pins on the bolts. Aiming at the above problems, cascade object detection method is adopted for pin defect detection in this paper, and improves the detection accuracy by gradually narrowing ROI (region of interest). The main contribution of this paper is to formulate a novel sample labelling criterion for cascade pin defect detection method. Building a sample set according to this criterion can not only greatly reduce the workload, but also improve the object detection accuracy. In this paper, YOLOv4 is used to validate the proposed method. The result shows that compared with the existing sample set building methods, the proposed sample labelling criterion improves the accuracy from 85.2% to 92% and recall from 85.7% to 94.2%.

A Fast and Accurate Method of Power Line Intelligent Inspection Based on Edge Computing

The combination of power intelligent inspection and edge computing plays a crucial role in the construction of power Internet of Things and transparent power grid. However, as a consequence of the low computing power of edge devices, the detection speed of the model running on edge devices tends to be slow. Accordingly, a fast and accurate method of power line edge intelligent inspection is proposed in this article. First, for key component detection of power lines, RepVGG, diverse branch block (DBB), efficient channel attention (ECA), and improved spatial pyramid pooling (SPP) are utilized to improve YOLOv5, giving rise to RepYOLO. In addition, to solve the problem of low accuracy in pin defect detection, a two-stage cascaded method is proposed. The first stage localizes various connection fittings in the input image, and the second stage recognizes normal pins and missing pins in these connection fittings. Finally, <inline-formula> <tex-math notation="LaTeX">$\text{C}++$ </tex-math></inline-formula> language combined with TensorRT is employed to optimize and accelerate the model on the NVIDIA Jetson Xavier NX embedded platform, fulfilling efficient power line edge intelligent inspection. Experimental results show that the TensorRT optimized RepYOLO algorithm is four times the inference speed of YOLOv5 with a 1.2&#x0025; increase in accuracy. Moreover, TensorRT optimized two-stage cascaded method can achieve accurate and real-time pin defect detection on edge devices.

Effect of tool material and process parameters on friction stir weld formation of maraging steel

ABSTRACT 18% Ni maraging steel is an ultra-high strength steel widely used in defence and aerospace applications. Fusion welding of maraging steel is encountered with a drop in properties due to the segregation of alloying element and hence, solid-state welding process such as friction stir welding (FSW) is a possible alternative. However, welding of high strength material using FSW is a challenge considering the tool material durability, tool material cost, and selection of tool geometry and process parameters. Therefore, the main focus of this paper is towards the selection of tool geometry, tool material, and process parameters that will result in least tool wear/failure in addition to obtaining defect-free welds of maraging steel using FSW. It was observed that polycrystalline cubic boron nitride (PCBN) and tungsten-molybdenum are durable tool material for welding maraging steel. It is recommended to use a minimum tool pin diameter (top 12 mm and bottom 8 mm) and shoulder diameter (20 mm) below which failure of pin and surface defects are encountered during FSW. It is also observed that there exists a narrow range of tool rotational speed (250–450 rpm) and tool travel speed (25 mm/min) at which defect-free maraging steel welds can be obtained.

Application of Data-Driven Iterative Learning Algorithm in Transmission Line Defect Detection

Deep learning technology has received extensive consideration in recent years, and its application value in target detection is also increasing day by day. In order to accelerate the practical process of deep learning technology in electric transmission line defect detection, the current work used the improved Faster R-CNN algorithm to achieve data-driven iterative training and defect detection functions for typical transmission line defect targets. Based on Faster R-CNN, we proposed an improved network that combines deformable convolution and feature pyramid modules and combined it with a data-driven iterative learning algorithm; it achieves extremely automated and intelligent transmission line defect target detection, forming an intelligent closed-loop image processing. The experimental results show that the increase of the recognition of improved Faster R-CNN network combined with data-driven iterative learning algorithm for the pin defect target is 31.7% more than Faster R-CNN. In the future, the proposed method can quickly improve the accuracy of transmission line defect target detection in a small sample and save manpower. It also provides some theoretical guidance for the practical work of transmission line defect target detection.

OEE improvement by pogo pin defect detection in wafer probing process

OEE improvement by pogo pin defect detection in wafer probing process

Detection of Pin Defects in Aerial Images Based on Cascaded Convolutional Neural Network

Pins are standard fasteners in power transmission lines, and the hidden dangers of pins falling off dramatically affects their safe operation. If a pin is missed, it is called pin defects in this paper. As the pin is a small target and has a complex background, traditional detection algorithms were used to identify pin defects from aerial images which suffer from poor accuracy and low efficiency. This paper proposed a target detection method based on cascaded convolutional neural networks. First, a small-scale shallow full convolutional neural network was used to obtain the region of interest; then, a deeper convolutional neural network conducted target classification and positioning on the obtained region of interest. Next, a nonlinear multilayer perceptron was introduced, the convolution kernel was decomposed, and the multi-scale feature maps were fused. At this point, an angle variable was added to the classification cross-entropy loss function. Multi-task learning and offline hard sample mining strategies were used in the training phase. The proposed method was tested on a self-built pin dataset and the remote sensing image RSOD dataset, and the experimental results proved its effectiveness. Our method can accurately identify pin defects in aerial images, thereby solving the engineering application problem of pin defect detection in transmission lines.

Detection method based on improved faster R-CNN for pin defect in transmission lines

Defects such as insulator, pins, and counterweight in highvoltage transmission lines affect the stability of the power system. The small targets such as pins in the unmanned aerial vehicle (UAV) inspection images of transmission lines occupy a small proportion in the images and the characteristic representations are poor which results a low defect detection rate and a high false positive rate. This paper proposed a transmission line pin defect detection algorithm based on improved Faster R-CNN. First, the pre-training weights with higher matching degree are obtained based on transfer learning. And it is applied to construct defect detection model. Then, the regional proposal network is used to extract features in the model. The results of defect detection are obtained by regression calculation and classification of regional characteristics. The experimental results show that the accuracy of the pin defect detection of the transmission line reaches 81.25%

Pin Defect Detection Method of UAV Patrol Overhead Line Based on Cascaded Convolution Network

Pin plays the role of fixing power equipment on the overhead line. Once it is missing, it will lay a serious hidden danger for the normal operation of the overhead line. In order to improve the efficiency of UAV patrol transmission line and improve the detection rate of pin defect of transmission line, this paper proposes a pin defect detection method based on cascaded convolution network. In view of the complex background of inspection image and the small size of pin, the overall detection method is divided into two parts: positioning and diagnosis. Firstly, all fastener positions including pins are located by the improved Faster-RCNN network, and then RetinaNet network is cascaded to diagnose the defects of the fastener. The difficulty of learning is decreased and the generalization ability is improved in this way. Finally the experiment shows that this method can effectively detect the pin defects in the UAV patrol image.