Local Defects Research Articles

Atomic-scale visualization of defect-induced localized vibrations in GaN.

Phonon engineering is crucial for thermal management in GaN-based power devices, where phonon-defect interactions limit performance. However, detecting nanoscale phonon transport constrained by III-nitride defects is challenging due to limited spatial resolution. Here, we used advanced scanning transmission electron microscopy and electron energy loss spectroscopy to examine vibrational modes in a prismatic stacking fault in GaN. By comparing experimental results with ab initio calculations, we identified three types of defect-derived modes: localized defect modes, a confined bulk mode, and a fully extended mode. Additionally, the PSF exhibits a smaller phonon energy gap and lower acoustic sound speeds than defect-free GaN, suggesting reduced thermal conductivity. Our study elucidates the vibrational behavior of a GaN defect via advanced characterization methods and highlights properties that may affect thermal behavior.

Time-varying displacement excitation and dynamic modeling of angular contact ball bearing with local defects

Angular contact ball bearings will produce fault damage when working for a long time, thus affecting the normal operation of the system. This paper takes angular contact ball bearings with local defects on the outer ring as the research object, proposes a method to distinguish different local defect profiles, establishes a generalized characterization model of time-varying displacement excitation of local defects in angular contact ball bearings, and studies the evolution process of local defects and their displacement excitation mechanism. On this basis, considering the influence of time-varying displacement caused by bearing defects on dynamic characteristics, based on Hertz contact theory, a fault dynamics model of angular contact ball bearings is established, and the correctness of the established model is verified by experiments. In addition, the analysis results show that rectangular local defects will eventually evolve into trapezoidal shapes; the displacement excitation change trends induced by different defect morphologies are different; compared with the length of local defects, the width has a greater impact on displacement excitation. The research results provide a theoretical basis for bearing optimization design and fault diagnosis.

Dynamic Modeling of Angular Contact Ball Bearing with Local Defects under EHL Considering Impact Force

In order to analyze the operating status of angular contact ball bearings(ACBBs) with local defects in more detail, a dynamic model of ACBB with local defects considering impact force under elastohydrodynamic lubrication conditions is proposed. Firstly, an elastohydrodynamic lubrication model for defective bearings considering spin is established, the film thickness and film stiffness of the defective bearings are calculated, and then time-varying displacement excitation model and time-varying stiffness model for local defects in angular contact ball bearings is presented. Based on this, an instantaneous impact force function related to defect size and bearing speed is established. Secondly, based on Hertz contact theory and impact force function, a dynamic calculation method for ACBBs with local defects on the outer ring is proposed. Finally, the vibration characteristics of ball bearings with faults are investigated, and the influence of different parameters on the dynamic response of the bearings is analyzed. The calculation results show that under lubrication conditions, the impact force on the vibration of the bearing is significantly weakened. With the increase of defect size and load, the frequency of bearing fault characteristics remain unchanged, but its amplitude increases. As the bearing speed increases, the characteristic frequency and amplitude of bearing faults increase.

The Fusiongram: a periodic weak fault feature extraction strategy and its application in bearing fault diagnosis

Abstract The weak periodic transient impact responses caused by localized defects in rolling bearings are often obscured by complex interferences, such as white noise, random transient impact responses, and periodic responses from system operations. Meanwhile, the fault feature information contributing to damage detection may be distributed across different frequency bands in the vibration signal. Therefore, under the influence of complex interference, it is a challenging problem regarding how to 1) accurately select the frequency bands containing rich fault feature information, 2) effectively extract damage information from different frequency bands, and thereby, 3) successfully achieve fault diagnosis for machinery condition monitoring. To address these issues, this research introduces a novel signal processing strategy, termed as Fusiongram, for extracting weak periodic fault features amidst the influence of complex interferences. Firstly, the method of complementary hierarchical decomposition is proposed, in which the signal is decomposed into multiple components with overlapping frequency contents. Then, an index with interference resistance is constructed to select the components carrying rich damage feature information. Finally, the adaptive threshold denoising and multicomponent normalized averaging techniques are employed to fuse the information from the squared envelope spectra of the selected components, thus obtaining the reconstructed squared envelope spectra for fault diagnosis. The Fusiongram is able to achieve the goal of weak fault feature extraction from signals with complex interference. The analysis results of numerical simulation and experimental testing verify the effectiveness and advantages of the proposed strategy.

Slow Equilibrium Relaxation in a Chiral Magnet Mediated by Topological Defects.

We performed a pump-probe experiment on the chiral magnet Cu_{2}OSeO_{3} to study the relaxation dynamics of its noncollinear magnetic orders, employing a millisecond magnetic field pulse as the pump and resonant elastic x-ray scattering as the probe. Our findings reveal that the system requires ∼0.2 s to stabilize after the perturbation applied to both the conical and skyrmion lattice phase, which is significantly slower than the typical nanosecond timescale observed in micromagnetics. This prolonged relaxation is attributed to the formation and slow dissipation of local topological defects, such as emergent monopoles. By unveiling the experimental lifetime of these emergent singularities in a noncollinear magnetic system, our study highlights a universal relaxation mechanism in solitonic textures within the slow dynamics regime, offering new insights into topological physics and advanced information storage solutions.

Smart structural health monitoring (SHM) system for on-board localization of defects in pipes using torsional ultrasonic guided waves

Most reported research for monitoring health of pipelines using ultrasonic guided waves (GW) typically utilize bulky piezoelectric transducer rings and laboratory-grade ultrasonic non-destructive testing (NDT) equipment. Consequently, the translation of these approaches from laboratory settings to field-deployable systems for real-time structural health monitoring (SHM) becomes challenging. In this work, we present an innovative algorithm for damage identification and localization in pipes, implemented on a compact FPGA-based smart GW-SHM system. The custom-designed board, featuring a Xilinx Artix-7 FPGA and front-end electronics, is capable of actuating the PZT thickness shear mode transducers, data acquisition and recording from PZT sensors and generating a damage index (DI) map for localizing the damage on the structure. The algorithm is a variation of the common source method adapted for cylindrical geometry. The utility of the algorithm is demonstrated for detection and localization of defects such as notch and mass loading on a steel pipe, through extensive finite element (FE) method simulations. Experimental results obtained using a C-clamp for applying mass loading on the pipe show good agreement with the FE simulations. The localization error values for experimental data analysed using C code on a processor implemented on the FPGA are consistent with algorithm results generated on a computer running Python code. The system presented in this study is suitable for a wide range of GW-SHM applications, especially in cost-sensitive scenarios that benefit from on-node signal processing over cloud-based solutions.

3D-Printed Shape Memory and Piezoelectric Bifunctional Thermoplastic Polyurethane/Polyvinylidene Fluoride Porous Composite Scaffold for Bone Regeneration.

Physical stimulations such as mechanical and electric stimulation can continuously work on bone defect locations to maintain and enhance cell activity, and it has become a hotspot for research in the field of bone repair. Herein, bifunctional porous composite scaffolds with shape memory and piezoelectric functions were fabricated using thermoplastic polyurethane (TPU) and poly(vinylidene fluoride) through triply periodic minimal surfaces design and selective laser sintering technology. Thereinto, the shape fixity ratio and recovery ratio of the composite scaffold reached 98.6% and 81.2%, respectively, showing excellent shape memory functions. More importantly, its piezoelectric coefficient (d33 = 2.47 pC/N) is close to the piezoelectric constant of bone tissue (d33 = 0.7-2.3 pC/N), and the voltage released during the compression process can reach 0.5 V. Furthermore, cyclic compression experiments showed that the strength of composite scaffold was up to 8.3 times compared with the TPU scaffold. Besides, the composite scaffold showed excellent cytocompatibility. In conclusion, the composite scaffold is expected to continuously generate mechanical and electric stimulation due to shape memory and piezoelectric function, respectively, which provide an effective strategy for bone repair.

Harmonic flow-field representations of quantum bits and gates

We describe a general procedure for mapping arbitrary n-qubit states to two-dimensional (2D) vector fields. The mappings use complex rational function representations of individual qubits, producing classical vector field configurations that can be interpreted in terms of 2D inviscid fluid flows or electric fields. Elementary qubits are identified with localized defects in 2D harmonic vector fields, and multiqubit states find natural field representations via complex superpositions of vector field products. In particular, separable states appear as highly symmetric flow configurations, making them both dynamically and visually distinct from entangled states. The resulting real-space representations of entangled qubit states enable an intuitive visualization of their transformations under quantum logic operations. We demonstrate this for the quantum Fourier transform and the period finding process underlying Shor's algorithm, along with other quantum algorithms. Due to its generic construction, the mapping procedure suggests the possibility of extending concepts such as entanglement or entanglement entropy to classical continuum systems, and thus may help guide new experimental approaches to information storage and nonstandard computation. Published by the American Physical Society 2024

Autonomous intelligent additive manufacturing of continuous fiber-reinforced composites: data-enhanced knowledgebase and multi-sensor fusion

ABSTRACT Additive manufacturing (AM) are an emerging technique to generate complex structures of composite. However, the instability of the AM process leads to adverse manufacturing effects, including dimensional inaccuracies and poor mechanical performance in CFRP composites. Online monitoring and adaptive control based on autonomous cognition are suggested to ensure the accuracy of as-fabricated parts and enhance their quality upon manufacturing. In this study, a method of online monitoring and self-adaptive control based on multi-sensor fusion is proposed to predict and adjust the process parameters during AM of CFRP. The force sensor, visual camera, and thermal camera are employed to obtain the multiple signals upon manufacturing and then realize the autonomous perception, cognition, and decision features. Herein, the quantitative correlations between local defects and multi-sensing features are established to guide the decision-making of closed-loop adjustment. Besides, an empirical surrogate model between the misalignment of fiber bundles and input parameters is built to predict the proper parameters. Moreover, the temperature difference and contact force are selected as the controlled features, which are acquired using a thermal camera and a force sensor. The proposed system offers a novel framework for bolstering both the stability of the AM process and the quality of fabricated components.

Real-time in-situ coatings corrosion monitoring using machine learning-enhanced triboelectric nanogenerator

Current methods for monitoring coating corrosion are limited by their inability to provide real-time data and dependence on external power sources. This study presents a novel in-situ corrosion monitoring system using a solid-liquid triboelectric nanogenerator (TENG) that converts mechanical energy into electrical signals for self-powered sensing. TENG signals and electrochemical impedance spectra were measured on a dopamine-modified lignin-polydimethylsiloxane coating on steel in 1 M NaCl solution under no corrosion, indentation, pitting, and broken conditions, respectively. We extract time-frequency features from the TENG signals to predict the coating’s corrosion condition by applying a customised convolutional neural network (CNN). By extracting time-frequency features from the TENG signals and applying a custom CNN, a prediction accuracy of 99 % for corrosion classification was achieved. Furthermore, the CNN regression model predicted coating impedance values with a high coefficient of determination (R² = 0.98), demonstrating its effectiveness in tracking corrosion progression. The developed TENG also facilitates defect localisation via a matrix electrode beneath the coating. Our approach introduces a promising real-time technology for in-situ corrosion monitoring.

Study on non-destructive visualization identification of concrete internal cracks based on SH array ultrasound

Ultrasonic nondestructive testing (NDT) technology was one of the most frequently used and fastest-developing NDT techniques in detection field. This method had high detection sensitivity, more accurate defect localization, and was harmless to the human body. With the development of ultrasonic testing technology, modern ultrasonic imaging technology generally had automatic data acquisition and processing functions, which allowed for intuitive and clear recognition of internal defects by the human eyes. The overall purpose of this study was to visualize the cracks in concrete by synthetic aperture focusing technique (SAFT) and full-waveform inversion (FWI) algorithms on the basis of ultrasonic nondestructive testing technology. At present, there have been studies on the use of ultrasonic instruments to detect the internal defects of concrete. However, the accuracy and authenticity of this method have not been compared in previous studies. Therefore, in this experiment, four concrete specimens with different buried depths, angles, shapes, and thicknesses of cracks were designed. Based on SH ultrasonic wave NDT technology, two types of operations were performed on the collected ultrasonic wave data: one was the application of SAFT method, and the other was the first application of FWI method for detecting internal cracks in concrete. The study found that both algorithms were most accurate in determining the burial depth of cracks, with error results not exceeding 10 %. They also showed good recognition effects for the angle, shape, and thickness of the buried cracks. This proved that both identification methods had the capability to recognize internal cracks. The Introview software bundled with the ultrasonic instrument could identify cracks using SAFT directly and quickly. When FWI was used with MATLAB computation, the results were more precise and intuitive. The overall purpose of this study was to detect cracks in concrete by SAFT and FWI algorithms on the basis of ultrasonic nondestructive testing technology. Combining the two crack identification techniques could be of great significance for ensuring the long-term safety and stability of concrete structures, providing strong support for structural maintenance and reinforcement.

Approach Based on the Ordered Fuzzy Decision Making System Dedicated to Supplier Evaluation in Supply Chain Management.

The selection of suppliers represents a pivotal aspect of supply chain management and has a considerable impact on the success and competitiveness of the organization in question. The selection of a suitable supplier is a multi-criteria decision making (MCDM) problem based on a number of qualitative, quantitative, and even conflicting criteria. The aim of this paper is to propose a novel MCDM approach dedicated to the supplier evaluation problem using an ordered fuzzy decision making system. This study uses a fuzzy inference system based on IF-THEN rules with ordered fuzzy numbers (OFNs). The approach employs the concept of OFNs to account for potential uncertainty and subjectivity in the decision making process, and it also takes into account the trends of changes in assessment values and entropy in the final supplier evaluation. This paper's principal contribution is the development of a knowledge base and the demonstration of its application in an ordered fuzzy expert system for multi-criteria supplier evaluation in a dynamic and uncertain environment. The proposed system takes into account the dynamic changes in the value of assessment parameters in the overall supplier assessment, allowing for the differentiation of suppliers based on current and historical data. The utilization of OFNs in a fuzzy model then allows for a reduction in the complexity of the knowledge base in comparison to a classical fuzzy system and makes it more accessible to users, as it requires only basic arithmetic operations in the inference process. This paper presents a comprehensive framework for the assessment of suppliers against a range of criteria, including local hiring, completeness, and defect factors. Furthermore, the potential to integrate sustainability and ESG (environmental, social, and corporate governance) criteria in the assessment process adds value to the decision making framework by adapting to current trends in supply chain management.

Research on Surface Defect Positioning Method of Air Rudder Based on Camera Mapping Model

(1) Background: Air rudders are used to control the flight attitude of aircraft, and their surface quality directly affects flight accuracy and safety. (2) Method: Traditional positioning methods can only obtain defect location information at the image level but cannot determine the defect’s physical surface position on the air rudder, which lacks guidance for subsequent defect repair. We propose a defect physical surface positioning method based on a camera mapping model. (3) Results: Repeated positioning experiments were conducted on three typical surface defects of the air rudder, with a maximum absolute error of 0.53 mm and a maximum uncertainty of 0.26 mm. Through hardware systems and software development, the real-time positioning function for surface defects on the air rudder was realized, with the maximum axial positioning error for real-time defect positioning being 0.38 mm. (4) Conclusions: The proposed defect positioning method meets the required accuracy, providing a basis for surface defect repair in the air rudder manufacturing process. It also offers a new approach for surface defect positioning in similar products, with engineering application value.

Damage and Crack Propagation Mechanism of Q345 Specimen Based on Peridynamics with Temperature and Bolt Holes

With the increasing demand for the performance and design refinement of steel structures (including houses, bridges, and infrastructure), many structures have adopted ultimate bearing capacity in service. The design service lives of steel building structures are generally more than 50 years, and most of them contain bolted connections, which suffer from extreme conditions such as fire (high temperature) during service. When the structure contains defects or cracks and bolt holes, it is easy to produce stress concentration at the defect location, which leads to crack nucleation and crack propagation, reduces the bearing capacity of the structure, and causes the collapse of the structure and causes disasters. In the process of structural damage and crack propagation, the traditional method has some disadvantages, such as stress singularity, the mesh needing to be redivided, and the crack being restricted to mesh; however, the integral method of peridynamics (PD) can completely avoid these problems. Therefore, in this paper, the constitutive equation of PD in high temperature is derived according to the variation law of steel material properties when changed by temperature increase and peridynamics parameters; the damage and crack expansion characteristics of Q345 steel specimens with bolt holes and a central double-crack at 20 °C, 200 °C, 400 °C, and 600 °C were analyzed to clarify the structural damage and failure mechanism. This study is helpful for providing theoretical support for the design of high-temperature steel structures, improving the stability of the structure, and ensuring the bearing capacity of the structure and the safety of people’s lives and property.

Defective Pennywort Leaf Detection Using Machine Vision and Mask R-CNN Model

Demand and market value for pennywort largely depend on the quality of the leaves, which can be affected by various ambient environment or fertigation variables during cultivation. Although early detection of defects in pennywort leaves would enable growers to take quick action, conventional manual detection is laborious and time consuming as well as subjective. Therefore, the objective of this study was to develop an automatic leaf defect detection algorithm for pennywort plants grown under controlled environment conditions, using machine vision and deep learning techniques. Leaf images were captured from pennywort plants grown in an ebb-and-flow hydroponic system under fluorescent light conditions in a controlled plant factory environment. Physically or biologically damaged leaves (e.g., curled, creased, discolored, misshapen, or brown spotted) were classified as defective leaves. Images were annotated using an online tool, and Mask R-CNN models were implemented with the integrated attention mechanisms, convolutional block attention module (CBAM) and coordinate attention (CA) and compared for improved image feature extraction. Transfer learning was employed to train the model with a smaller dataset, effectively reducing processing time. The improved models demonstrated significant advancements in accuracy and precision, with the CA-augmented model achieving the highest metrics, including a mean average precision (mAP) of 0.931 and an accuracy of 0.937. These enhancements enabled more precise localization and classification of leaf defects, outperforming the baseline Mask R-CNN model in complex visual recognition tasks. The final model was robust, effectively distinguishing defective leaves in challenging scenarios, making it highly suitable for applications in precision agriculture. Future research can build on this modeling framework, exploring additional variables to identify specific leaf abnormalities at earlier growth stages, which is crucial for production quality assurance.

Fatigue properties of a Ti–5Al–5Mo-5 V–3Cr alloy manufactured by electron beam powder bed fusion

AbstractAdditive manufacturing (AM) is a modern way of manufacturing structures, which tends to have fewer design limitations than those manufactured by conventional processes such as casting or forging. A combination of high-strength materials and small and complex structures opens up a wide range of potential applications, especially in the fields of medicine and aerospace. Titanium and its alloys show a very beneficial combination of density and mechanical properties. One of these alloys is the metastable β titanium alloy Ti– 5Al–5Mo–5 V–3Cr (Ti-5553), which is currently used mainly for large forged structures like landing gears of airplanes. In this study, for the first time the fatigue behavior of electron beam powder bed fused (PBF-EB) Ti-5553 was investigated with a focus on the defects created by the layer wise manufacturing. To understand the defect structure and its respective influence on the fatigue behavior, all specimens were scanned prior to fatigue testing using a state-of-the-art µ-focus CT. The specimens were subjected to two heat treatment procedures commonly used in technical applications, which were aiming for high strength (solution treated and aged—STA) as well as high ductility (beta annealed, slow cooled and aged—BASCA). Results indicate that the fatigue strength of PBF-EB manufactured Ti-5553 is significantly reduced compared to conventionally manufactured Ti-5553. The main reason for this are defects, which have varying critical effects depending on the heat treatment of the specimen and the defect size, shape, location and type.

Research on defect image inversion method based on multiple lift-off values magnetic memory signals

Metal magnetic memory (MMM) technique can detect macroscopic defects and stress concentration zones of ferromagnets, and it can identify the approximate positions of these flaws, while a little further information about defects characteristics can be provided. To promote study in this area, defects were modeled as located magnetic dipoles whose strength and locations should be determined, and the technique of truncated generalized inverse and reduced space inversion were used to image the dipole strength. Through the simulation experiment, we found that the inversion image quality was influenced by the lift-off value. When the magnetic field data for inversion was measured with a small lift-off value, the inner dipoles contributions were easily buried and the inversion image can only shown the surface dipole well. In contrast, if the magnetic field data was measured with a greater lift-off value, it was possible to inverse the deep dipoles while the image was somewhat out of focus. To overcome this limitation, we composed the magnetic field data measured under different lift-off values together for inversion. We used the same model to test this approach and applied it on real crack defects magnetic field data. It demonstrates that the new inversion approach shows better accuracy than the single lift-off value and it was possible to infer the location and size of defect by the evaluation of preset dipoles.

Braids and higher-order exceptional points from the interplay between lossy defects and topological boundary states

We show that the perturbation of the Su-Schrieffer-Heeger chain by a localized lossy defect leads to higher-order exceptional points (HOEP). Depending on the location of the defect, third- and fourth-order exceptional points (EP3 and EP4) appear in the space of Hamiltonian parameters. On the one hand, they arise due to the non-Abelian braiding properties of exceptional lines (EL) in parameter space. Namely, the HOEPs lie at intersections of mutually noncommuting ELs. On the other hand, we show that such special intersections happen due to the fact that the delocalization of edge states, induced by the non-Hermitian defect, hybridizes them with defect states. These can then coalesce together into an EP3. When the defect lies at the midpoint of the chain, a special symmetry of the full spectrum can lead to an EP4. In this way, our model illustrates the emergence of interesting non-Abelian topological properties in the multiband structure of non-Hermitian perturbations of topological phases. Published by the American Physical Society 2024

Features of diagnosis and treatment of a polytrauma victim with predominant closed chest trauma with lung and diaphragm rupture. Clinical case

The article describes a clinical case and presents clinical signs of traumatic rupture of the diaphragm and lung on the background of a wave‑like course of the postoperative period. Open diaphragmatic injuries are more common than closed ones. In this case, the closed chest and abdominal trauma was sustained as a result of a road traffic accident. The injury was combined and severe, with signs of traumatic shock. The location of the diaphragmatic injury was on the right side, which is less common. The severe condition of the patient with respiratory failure (respiratory rate over 30 per minute) was an indication for artificial lung ventilation, which made it impossible to take complaints and anamnesis. The individual spatial topography of the diaphragm depends on the size and location of the abdominal organs, body structure, and depends on the line of examination. The movement of internal organs into the pleural cavity indicates a diaphragmatic rupture, but in this case, the extrahepatic location of the diaphragmatic defect was covered by the liver, the lower lobe of the right lung, and adhesions, which led to the cover­up of the diaphragmatic defect. Increased abdominal size due to polytrauma and mechanical ventilation in case of closed chest and abdominal trauma; increased air discharge through pleural drainage during video laparoscopy or increased abdominal size during video thoracoscopy; clamping of the pleural drainage with a spiral computed tomography of the chest and abdominal organs allows detecting pneumoperitoneum and pneumothorax, which indicates the presence of a defect in the diaphragm and lung. The use of video thoracoscopy, video laparoscopy, and spiral computed tomography does not always provide complete information about the existing damage to the diaphragm, so dynamic observation with control radiological examinations is preferred.

Identifying Rare Events in Quantum Molecular Dynamics of Nanomaterials with Outlier Detection Indices.

Nanoscale and condensed matter systems evolve on multiple length- and time-scales, and rare events such as local phase transformation, ion segregation, defect migration, interface reconstruction, and grain boundary sliding can have a profound influence on material properties. We demonstrate how outlier detection indices can be used to identify rare events in machine-learning based, high-dimensional molecular dynamics (MD) simulations. Designed to order data-points from typical to untypical, the indices enable one to capture atomic events that are hard to detect otherwise. We demonstrate the approach with a nanosecond MD simulation of a grain boundary in a metal halide perovskite that is extensively studied for solar energy and optoelectronic applications. The method captures the initial grain boundary sliding and a spontaneous fluctuation half a nanosecond later, both events giving rise to persistent deep electronic trap states that impact charge carrier lifetime and transport and material performance. The approach offers a generalizable and simple method for identifying outlier events in complex condensed matter, molecular, and nanoscale systems.