Magnetic Defects Research Articles

MTGGAN: generate type-controllable magnetic tile defect data to improve defect detection performance

ABSTRACT The scarcity of training samples and label information poses a major challenge for deep learning defect detection technology. Using generative models to expand sample data is considered an effective approach to address this issue. However, existing GAN models struggle to generate high-fidelity and type-controllable magnetic tile defect samples. Therefore, this paper proposes a type-controllable magnetic tile generation method (MTGGAN) that generates data with inherent label information. The model enhances fidelity and diversity in conditional generation through dual discriminator, auxiliary classifier, and auxiliary generator mechanisms. Additionally, focal loss is introduced to address the model’s class tendency issue during training. Extensive experiments demonstrate that, compared to advanced GAN methods, samples generated by MTGGAN exhibit superior quality and diversity. Furthermore, the generated data can train superior detection models. Effectively alleviating the problem of data scarcity in defect detection.

Topological magnetic defects in a strong permanent magnet Nd2Fe14B

Skyrmions, highly mobile and manipulable magnetic objects, have garnered significant interest for their potential applications in modern magnetic device technology. However, their formation has been limited to a small number of materials due to the delicate balance of magnetic energies involved. In this study, we report the irreversible formation of skyrmions in Nd2Fe14B, a rare-earth permanent magnet characterized by large magnetic anisotropy. Remarkably, these magnetic topological defects exist over a wide range of temperatures and magnetic fields (295–565 K and 0–0.1 T) due to the high Curie temperature and significant magnetic anisotropy of Nd2Fe14B. Our findings not only unveil a new material where skyrmions can form but also open up possibilities for exploring topological defects in various magnetic systems, including strong hard magnets.

Stochastic hexagonal injectors in artificial spin ice

Artificial spin ice (ASI) systems have emerged as powerful platforms for exploring the fundamental aspects of magnetic frustration and topological phenomena in condensed matter physics. In this study, we investigate the bountiful effects that result from introducing hexagonal magnetic defects into an ASI lattice. The stochastic stabilisation of a plethora of metastable states in the hexagonal defects are explored, as well as harnessing the defect magnetisation state for the selective injection of emergent monopoles of different polarities and proximities within the lattice. We demonstrate a mechanism for tailoring the ASI behaviour using the magnetic state of the defect, which is of interest to applications including magnetic memory devices and spin-based logic.

A lightweight parallel attention residual network for tile defect recognition

In modern industrial production, permanent magnet motors are an indispensable part of industrial manufacturing. The quality of the magnetic tiles directly affects the working performance of the permanent magnet motors, making the detection of defects on the surface of magnetic tiles critically important. However, due to the small size of defects on the tile image and the reflectivity of the defective surface, the details of image characteristics are not prominently acquired.These problems bring a lot of difficulties for the recognition of magnetic tile defects. In this paper, a magnetic tile defect detection method is proposed for the probAlems of unclear image features and small defects. First, the image is processed using linear variation to enhance the image detail features. Then, by introducing the inverted bottleneck block structure in MobileNetV2, the Attention Parallel Residual Convolution Block (APR) is proposed, and the Lightweight Parallel Attention Residual Network (LPAR-Net) is built. In APR Block, 7 × 7 convolution is introduced so that the model can extract spatial features from a larger range, and weighted fusion of input images by residual structure. In addition, in this paper, CBAM is improved, split into two parts and inserted into APR Block. Finally, the mainstream image classification models and the LPAR-Net proposed in this paper are used for comparison, respectively. The experimental results show that the method achieves 93.63% accuracy on the adopted dataset, which is better than the existing mainstream image classification network models DenseNet, MobileNet, ConvNext and so on. In addition, this paper introduces a strip steel surface defect dataset and compares it with the above image classification model, which verifies that the detection method proposed in this paper still has strong recognition capability.

MT-U2Net: Lightweight detection network for high-precision magnetic tile surface defect localization

Automated surface defect localization is closely related to product quality control. Although many image segmentation models have achieved excellent results in industrial defect localization, the wide-ranging defects in magnetic tile materials and the similarity between textures and background textures make it difficult to balance detection model performance and model size. Therefore, we propose a new magnetic tile defect localization model called MT-U2Net. The model consists of three parts: Dres-Ublock, U2 structure, and attention mechanism. Dres-Ublock is used to enhance the model's feature extraction capability and reduce its size; the U2-Net structure is utilized to learn cross-scale defect information; the attention mechanism is employed to eliminate redundant information. MT-U2Net improves detection performance across various defect scales. In the test set, MT-U2Net achieves an F1 Score of 0.840 and an MAE of 0.024 and compresses the model size to 3.39 M. Extensive experimental results show that MT-U2Net outperforms existing state-of-the-art algorithms in magnetic tile defect detection performance and model size.

Magnetic Domain and Structural Defects Size in Ultrathin Films

Herein, a model is proposed for measuring the structural defects size r0 in an ultrathin magnetic layer with perpendicular magnetic anisotropy. Based on the observations of magnetic domains in Ta/Pt/Co/Pt ultrathin films, using polar magneto‐optical Kerr effect microscopy and measurements of their magnetic anisotropies, the correlation between magnetic domains size D and structural defects size r0, as well as the defects concentration parameter αK, which designates the degree of pinning, has been modeled. The average r0 value found is high in the sample with unannealed buffer layers and considerably decreases with annealing. It is 6.17 nm with unannealed Ta/Pt buffer layers, 1.06 nm in sample with Ta/Pt buffer layers annealed at 423 K, and 0.49 nm in that with buffer layers annealed at 573 K. The significant drop of r0 is in good agreement with the high depinning noted with buffer layers annealing in recent work.

Reflection-type orientation-switchable optical differentiator exemplified by the BK7-MoS2 interface

Optical differential operation is an ultra-fast edge information extraction technology that enables identifying the image features, whereas current optical differentiators mainly operate along one or two differential orientations and is incompetent for switching the differential orientation. A reflection-type orientation-switchable optical differentiator is proposed and the BK7-MoS2 interface is used as an example to analyze the edge detection performance. Theory suggests that there are dual modulation methods, one is tuning the incident polarization angle with a Brewster incident angle, and the other one is altering the incident angle with a nearly vertical incident polarization angle. The reason is that the term r p /r s tan α ≪ 1 is an ignored small quantity under these two situations and the spatial spectral transfer function is closely related to the scale coefficients C x and C y , whose magnitudes are both the incident polarization angle and incident angle dependent. Our optical differentiator can be combined with other tuning means since some two-dimensional materials are responsive to external stimuli, such as electrical or magnetic fields and defect engineering, these findings have potential applications in optical sensing, machine vision, and biomedical imaging.

Overview of welding defect detection utilising metal magnetic memory technology

Welded joints frequently endure the composite stress of steel components. The presence of defects within these welded joints can significantly jeopardise the safety and performance of the welded structure. Magnetic memory testing technology has garnered substantial attention due to its ability to evaluate welding defects. However, the conventional zero-point pole theory, which serves as the foundation for defect assessment in practical detection, may lead to defect location and omission errors. In response to this challenge, scholars have conducted extensive research to accurately pinpoint the location and identify the types of defect within welds. This paper systematically reviews the mechanisms of magnetic memory welding defect detection, the factors that influence it, signal characteristic parameters, noise reduction in magnetic memory signals and the application of machine learning for quantitative assessment. By summarising these research advancements, this paper aims to address the current issues and provide guidance for the precise quantitative evaluation of welding defects in the future using metal magnetic memory technology.

DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection

DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection

Unidirectional focusing Rayleigh waves EMAT for plate surface defect Inspection

ABSTRACT Electromagnetic ultrasonic Rayleigh waves are commonly used for detecting surface or near-surface defects, but they still exhibit the drawback of low energy conversion efficiency. In this study, based on the Huygens principle and the discrete point source method, a double curving meander line coils EMAT (DC-MLCs EMAT) which generate unidirectional focusing Rayleigh waves is designed. Firstly, a simulation model of the transducer is established using COMSOL Multiphysics. Then, the impacts of magnet and coise defect detection efficiency is validated by numerical simulation method. Finally, a physical transducer is made, an experimental system is built, and the defect specimens are tested using this experimental system. The simulation and experimental results show that the optimised DC-MLCs EMAT significantly improves ultrasound focusing effect. Compared with the existing single constant length meander line coil（SCL-MLC）EMAT, the signal strength increases by 164.36%. This DC-MLCs EMAT shows good application prospects in the field of fixed focal length measurement.

Solving conformal defects in 3D conformal field theory using fuzzy sphere regularization

Defects in conformal field theory (CFT) are of significant theoretical and experimental importance. The presence of defects theoretically enriches the structure of the CFT, but at the same time, it makes it more challenging to study, especially in dimensions higher than two. Here, we demonstrate that the recently-developed theoretical scheme, fuzzy (non-commutative) sphere regularization, provides a powerful lens through which one can dissect the defect of 3D CFTs in a transparent way. As a notable example, we study the magnetic line defect of 3D Ising CFT and clearly demonstrate that it flows to a conformal defect fixed point. We have identified 6 low-lying defect primary operators, including the displacement operator, and accurately extract their scaling dimensions through the state-operator correspondence. Moreover, we also compute one-point bulk correlators and two-point bulk-defect correlators, which show great agreement with predictions of defect conformal symmetry, and from which we extract various bulk-defect operator product expansion coefficients. Our work demonstrates that the fuzzy sphere offers a powerful tool for exploring the rich physics in 3D defect CFTs.

Skyrmionium dynamics on a racetrack in the presence of a magnetic defect

Exotic topological textures known as skyrmioniums can stabilize in different types of ferromagnetic nanostructures. They possess a topological charge of Q=0 and can move in straight lines unaffected by the skyrmion Hall effect. In this work, using micromagnetic simulations, we study the behavior of a skyrmionium when a polarized spin current transports it along a racetrack in the presence of a semicircular magnetic defect with different Dzyaloshinskii–Moriya interaction values. As a first step, we obtained the perpendicular uniaxial anisotropy (Kz) and the Dzyaloshinskii–Moriya constants (Dint) for which it is possible to form a skyrmionium in the racetrack. Our results show that the higher the Kz value, the higher the Dint values necessary to stabilize the skyrmionium. Furthermore, interaction energies between the skyrmionium and the semicircular defect are presented. The results show that the interaction energy can be attractive or repulsive, depending on the Dzyaloshinskii–Moriya defect interaction constant (Dintdef). Finally, we conclude that using these types of defects and a spin-polarized current, the motion of the skyrmionium can be either modified in a specific way or even blocked.

Real space iterative reconstruction for vector tomography (RESIRE-V)

Tomography has had an important impact on the physical, biological, and medical sciences. To date, most tomographic applications have been focused on 3D scalar reconstructions. However, in some crucial applications, vector tomography is required to reconstruct 3D vector fields such as the electric and magnetic fields. Over the years, several vector tomography methods have been developed. Here, we present the mathematical foundation and algorithmic implementation of REal Space Iterative REconstruction for Vector tomography, termed RESIRE-V. RESIRE-V uses multiple tilt series of projections and iterates between the projections and a 3D reconstruction. Each iteration consists of a forward step using the Radon transform and a backward step using its transpose, then updates the object via gradient descent. Incorporating with a 3D support constraint, the algorithm iteratively minimizes an error metric, defined as the difference between the measured and calculated projections. The algorithm can also be used to refine the tilt angles and further improve the 3D reconstruction. To validate RESIRE-V, we first apply it to a simulated data set of the 3D magnetization vector field, consisting of two orthogonal tilt series, each with a missing wedge. Our quantitative analysis shows that the three components of the reconstructed magnetization vector field agree well with the ground-truth counterparts. We then use RESIRE-V to reconstruct the 3D magnetization vector field of a ferromagnetic meta-lattice consisting of three tilt series. Our 3D vector reconstruction reveals the existence of topological magnetic defects with positive and negative charges. We expect that RESIRE-V can be incorporated into different imaging modalities as a general vector tomography method. To make the algorithm accessible to a broad user community, we have made our RESIRE-V MATLAB source codes and the data freely available at https://github.com/minhpham0309/RESIRE-V.

Attention-based convolution neural network for magnetic tile surface defect classification and detection

Effectively identifying surface defects in magnetic tiles has proven to be highly challenging due to limited sample availability and irrelevant background interference, which also plays a crucial role in significantly influencing the lifespan and reliability of permanent magnet motors. To address these challenges, our study draws inspiration from a comprehensive analysis of the retinal attention mechanism and proposes three guiding criteria: multi-level resolution, what to look for, and where to look at. These criteria are utilized as foundational principles to enhance the representation learning capability of designed neural network structures through the incorporation of the retinal attention mechanism. Subsequently, based on these guiding criteria, we introduce a novel convolutional retinal attention block (CRAB) to learn discriminative and robust feature representations for magnetic tile surface defect classification and detection. The proposed CRAB comprises three modules: multi-resolution module (MRM), global attention aggregation module (GAAM), and local attention aggregation module (LAAM), designed to extract discriminative and robust features by refining meaningful information and suppressing redundant ones. Comprehensive experimental results across image classification and object detection tasks demonstrate that the proposed CRAB outperforms existing methods such as SE, ECA, and CBAM, and can effectively amplify the representation power across various backbone networks, including VGG-16, GoogLeNet, ResNet-18, and ResNet-50. An evaluation on surface defect classification and detection tasks for industrial magnetic tiles further shows that CRAB achieves accuracies of 99.50% and 96.98%, respectively. These results emphasize the promising application prospects of the proposed method in detecting industrial surface defects amid expansive and inconsequential backgrounds. The code of the proposed method is available at: https://github.com/KWflyer/CRAB.

Defect Recognition Method for Magnetic Leakage Detection in Oil and Gas Steel Pipes Based on Improved Neural Networks

The aging infrastructure of petroleum and natural gas pipelines poses a threat to national economies, necessitating precise defect detection for safety and efficiency. To enhance the accuracy of predicting pipeline defect sizes, this study introduces a magnetic leakage detection system, employing Backpropagation (BP) neural networks optimized with genetic algorithms. Traditional BP networks face challenges, including parameter determination and slow convergence, addressed through genetic algorithms' global search capabilities. Simulated data are generated using ANSYS software by using models of semi-circular defects in steel pipes, producing magnetic leakage signals of varying intensities. MATLAB was used to construct both standard BP and genetically optimized BP neural networks. Results show that the latter significantly reduces computational errors, demonstrating improved accuracy in defect dimension prediction. The approach contributes to overcoming non-uniqueness in the recognition process and the complex nonlinear relationship between magnetic signals and defect size parameters. The study offers a guided approach for selecting BP neural network parameters, enhancing practicality. Simulations validate the method's effectiveness, indicating low workload and high reliability. This research provides a meaningful advancement in the detection of defects in long-distance pipelines, impacting the safety and efficiency of petroleum and natural gas transportation.

Superspin chains solutions from 4D Chern-Simons theory

As a generalisation of the correspondence linking 2D integrable systems with 4D Chern-Simons (CS) gauge theory, superspin chains are realized by means of crossing electric and magnetic super line defects in the 4D CS with super gauge symmetry. The oscillator realization of Lax operators solving the RLL relations of integrability is obtained in the gauge theory by extending the notion of Levi decomposition to Lie superalgebras. Based on particular 3-gradings of Lie superalgebras, we obtain graded oscillator Lax matrices for superspin chains with internal symmetries given by A(m − 1 | n − 1), B(m | n), C(n) and D(m | n).

Magnetic Flux Leakage Defect Identification Method for Small-Diameter Pipeline Elbow Based on the Improved YOLOv5

Abstract The elbow is an important constituent of oil and gas pipeline systems and plays a key role in changing the direction of pipelines. Corrosion defects pose a significant risk to the safe operation of elbows. Magnetic flux leakage (MFL) detection has been developed as a suitable technique for identifying defects in pipelines. To address the distortion of elbow defect signals in the images arising from variations in the liftoff value of the leakage detector, this paper proposed an image identification method based on an improved YOLOv5 network. The differences in defect images are simulated by analyzing the liftoff value of the magnetization unit. A defect image enhancement method of multiscale retinex with color restoration fusion homomorphic filtering (MSRCR-HF) is employed to enhance the features of defective MFL signal images. To further improve the accuracy of the model, the YOLOv5 network is optimized by integrating the convolutional block attention module (CBAM) and the space-to-depth-nonstrided convolution (SPD-Conv) module. The results show that the proposed image enhancement method effectively accentuates the features of defect images. Moreover, the suggested image identification method exhibits superior accuracy in identification. The mean average precision (mAP) values for the original image set and the enhanced image set are 85.0% and 91.4%, respectively. Consequently, the proposed method is shown to be highly viable for the automatic identification of MFL defects in small-diameter pipe elbows.

Review of High Dimensional Neural Network Potentials

Over the past few years, machine learning potentials (MLPs) have been fully developed and can now be applied to a variety of large-scale atomic simulations. MLPs encompass a number of different machine learning algorithms, of which neural network potentials are the most dominant and representative, and have achieved great success. This review focuses on the second generation of neural network potentials, high-dimensional neural network potentials (HDNNPs). While HDNNP has accomplished noteworthy results in the implementation of ion diffusion, nuclear magnetic resonance parameters, defect formation, and thermal conductivity, it still encounters hurdles in the compilation of training datasets, transferability, accuracy constraints, and the management of multiple chemical species. In spite of this, HDNNP remains a highly promising methodology. Looking forward, we anticipate the development of more specialized software for HDNNP to augment research efficiency and lower the barrier to usage.

A global centralized magnetic flux leakage small defect detection network

Abstract To solve the problem that magnetic-flux-leakage (MFL) small defects are difficult to accurately detect by machine learning methods, a global centralized magnetic flux leakage small defect detection network (RCFPNet) is proposed. RCFPNet consists of simulation data enhancement, improved feature extraction (backbone), an improved centralized feature pyramid (CFP) and a detection head network. The MFL defect data of various scales and shapes are simulated by ANSYS simulation software and superimposed with the actual detected MFL defects to expand the dataset. The Repvgg module is used to replace the 3*3 convolution of the backbone to improve the detection speed. An improved spatially explicit vision center scheme (EVC) and a global centralized regulation rule (GCR) for feature fusion networks are proposed for feature fusion networks. RCFPNet is based on an improvement of the YOLOv5 network. Experiments have proven that RCFPNet has improved detection speed and accuracy and has achieved good results in the detection of magnetic leakage small defects. Experiments show that when the IOU = 0.5, the accuracy rate of this algorithm is 96.1%, and the reasoning time is 8.9 ms.

Shot Noise in Helical Edge States in Presence of a Static Magnetic Defect

The Fano factor, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal{F},$$\\end{document} of the shot noise of the current through the edge states of a two-dimensional topological insulator with contacts of generic type is calculated. A magnetic static defect changes \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal{F}$$\\end{document} significantly. For metallic contacts, as the strength of the defect increases, the Fano factor increases from \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal{F} = 0$$\\end{document} to its maximum value, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\mathcal{F}}_{{{\ ext{max}}}}} \\approx 0.17,$$\\end{document} and then decreases back to zero value in the limit of strong defect. For tunnel contacts in the limit of weak tunnel coupling, the Fano factor is insensitive to the strength of the defect: \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal{F} \ o 1{\ ext{/}}2.$$\\end{document} For weak but finite tunnel coupling strength, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal{F}$$\\end{document} exhibits a periodic series of sharp peaks of small amplitude as a function of the magnetic flux piercing the sample. The peaks transform into Aharonov–Bohm harmonic oscillations with increasing the strength of the tunnel coupling.