A Methodology for Identifying Defects in Wire Rope Based on Permanent Magnet Excitation

A wire rope defects detection method based on permanent magnet excitation is proposed.A detection system,mainly composed of permanent magnet excitation,distance detection,multi-sensor magnetic flux leakage signal acquisition and data analysis device,is set up.According to the different characteristics of the multi-sensor magnetic flux leakage signal,the localized fault(LF)and loss of metallic cross-sectional area(LMA)signal is separated,and then the two defects can be detected.The experiments show that the method can effectively detect the two defects when they appear simultaneously on the wire rope.

Topological hierarchy matters — topological matters with superlattices of defects

Topological insulators/superconductors are new states of quantum matter with metallic edge/surface states.In this paper,we review the defects effect in these topological states and study new types of topological matters — topological hierarchy matters.We find that both topological defects（quantized vortices） and non topological defects（vacancies） can induce topological mid-gap states in the topological hierarchy matters after considering the superlattice of defects.These topological mid-gap states have nontrivial topological properties,including the nonzero Chern number and the gapless edge states.Effective tight-binding models are obtained to describe the topological mid-gap states in the topological hierarchy matters.

Quantitative Evaluation of Fatigue Crack Size Using Eddy Current Pulsed Thermography

A set of metallic specimens containing fatigue cracks with different sizes were tested using eddy current pulsed thermography(ECPT),therefore the relations between heating response of the crack area and the crack length was studied.The numerical and experimental results both showed that the increase of the crack length enhanced the crack heating response under specific test conditions.A particular form of calculated response signal,which is linearly related to the crack length,was introduced to provide a quantitative evaluation of crack length.

Research on Electromagnetic Acoustic Emission Signal Recognition Based on Local Mean Decomposition and Least Squares Support Vector Machine

Electromagnetic acoustic emission technology is one of nondestructive testing, which can be used for defect detection of metal specimens. In this study, round and cracked metal specimens, round metal specimens, and intact metal specimens were prepared. And the electromagnetic acoustic emission signals of the three specimens were collected. In addition, the local mean decomposition(LMD), Autoregressive model(AR model) and least squares support vector machine (LSSVM) algorithms were combined to identify the eletromagnetic acoustic emission signals of round and cracked, round, and intact specimens. According to the algorithm recognition results, the recognition accuracy of can reach above 97.5%, which has a higher recognition rate compared with SVM and BP neural network. The results of the study show that the algorithm is able to identify quickly and accurately crack defect in metal specimens.

Steel Surface Defect Detection Using Learnable Memory Vision Transformer

This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.

Defectivity control of aluminum chemical mechanical planarization in replacement metal gate process of MOSFET

The replacement metal gate（RMG） defectivity performance control is very challenging in high-k metal gate（HKMG） chemical mechanical polishing（CMP）. In this study, three major defect types, including fall-on particles, micro-scratch and corrosion have been investigated. The research studied the effects of polishing pad,pressure, rotating speed, flow rate and post-CMP cleaning on the three kinds of defect, which finally eliminated the defects and achieved good surface morphology. This study will provide an important reference value for the future research of aluminum metal gate CMP.

Defect energetics and magnetic properties of 3d-transition-metal-doped topological crystalline insulator SnTe

The introduction of magnetism in SnTe-class topological crystalline insulators is a challenging subject with great importance in the quantum device applications. Based on the first-principles calculations, we have studied the defect energetics and magnetic properties of 3d transition-metal(TM)-doped SnTe. We find that the doped TM atoms prefer to stay in the neutral states and have comparatively high formation energies, suggesting that the uniform TMdoping in SnTe with a higher concentration will be difficult unless clustering. In the dilute doping regime, all the magnetic TMatoms are in the high-spin states, indicating that the spin splitting energy of 3d TM is stronger than the crystal splitting energy of the SnTe ligand. Importantly, Mn-doped SnTe has relatively low defect formation energy, largest local magnetic moment, and no defect levels in the bulk gap, suggesting that Mn is a promising magnetic dopant to realize the magnetic order for the theoretically-proposed large-Chern-number quantum anomalous Hall effect(QAHE) in SnTe.

Insight on Viscoelasticiy of Ti16.7Zr16.7Hf16.7Cu16.7Ni16.7Be16.7 High Entropy Bulk Metallic Glass

High entropy bulk metallic glasses show promising mechanical and physical properties.Dynamic mechanical properties of Ti_（16.7）Zr_（16.7）Hf_（16.7）Cu_（16.7）Ni_（16.7）Be_（16.7）high entropy bulk metallic glass were investigated by mechanical spectroscopy（or called dynamic mechanical analysis）.The main（α）relaxation was observed in the framework of the loss modulus G″,which is related to the dynamic glass transition behaviour for the glassy materials.From physical model point of view,dynamic mechanical properties of the Ti_（16.7）Zr_（16.7）Hf_（16.7）Cu_（16.7）Ni_（16.7）Be_（16.7）high entropy bulk metallic glass show good agreement compared with the quasi-point defects theory.