Defects In Materials Research Articles

A Comprehensive Review and Future Perspectives of Simulation Approaches in Wire Arc Additive Manufacturing (WAAM)

Abstract Wire arc additive manufacturing (WAAM) has emerged as a promising technique for producing large-scale metal components, favoured by high deposition rates, flexibility and low cost. Despite its potential, the complexity of WAAM processes, which involves intricate thermal dynamics, phase transitions, and metallurgical, mechanical, and chemical interactions, presents considerable challenges in final product qualities. Simulation technologies in WAAM have proven invaluable, providing accurate predictions in key areas such as material properties, defect identification, deposit morphology, and residual stress. These predictions play a critical role in optimizing manufacturing strategies for the final product. This paper provides a comprehensive review of the simulation techniques applied in WAAM, tracing developments from 2013 to 2023. Initially, it analyzes the current challenges faced by simulation methods in three main areas. Subsequently, the review explores the current modelling approaches and the applications of these simulations. Following this, the paper discusses the present state of WAAM simulation, identifying specific issues inherent to WAAM simulation itself. Finally, through a thorough review of existing literature and related analysis, the paper offers future perspectives on potential advancements in WAAM simulation strategies.

Characterization and Fault Diagnosis of Vibration Characteristics of Tenon Disks with Cracks Based on Blade Tip Timing

Abstract The tenon-structured disk is a critical component of the rotor system in aeroengines. Under the repeated cyclic loads and inherent material defects, the disk is susceptible to the initiation of fatigue cracks. The impact of disk cracks on the shaft vibration response is subtle and challenging to monitor, however, it can induce periodic vibrations in the blades near the crack location. Therefore, this paper proposes a method for crack identification in rotor disks based on Blade Tip Timing (BTT) technology, which allows for the reflection of rotor disk crack parameters (type, location) through the blade tip vibration parameters, enabling online monitoring and fault diagnosis of rotor disk cracks. To guide the implantation of cracks, this study analyzes the weak points of the disk and conducts a comparative analysis of the effects of cracks of different depths on the blade tip vibration characteristics. Experimental validation confirms the accuracy of the proposed method, providing theoretical support for the onboard online identification of cracks in aeroengine disks.

A Quantification Method for Micro-defect Size in Copper Foil Based on Motion-induced Eddy Current Signal Characteristics

Abstract Lithium batteries represent a pivotal technology in modern energy storage and have attracted significant attention. Copper foil is frequently employed as the current collector for the negative electrode; however, the presence of micro-defects can severely impair the electrochemical performance of lithium batteries. Therefore, developing a method that can accurately detect micro-defects during high-speed production processes and subsequently achieve precise quantification of defect sizes is of paramount importance. Motion-Induced Eddy Current (MIEC) techniques, characterized by relative motion, can detect defects in conductive materials during high-speed movement. However, no research has been conducted on defect size quantification based on motion-induced eddy current signals. Consequently, this study proposes a quantification method for micro-defect size in copper foil based on motion-induced eddy current signal characteristics. By combining wavelet soft-thresholding and morphological filtering, high-frequency noise and signal fluctuations caused by variations in lift-off distance are effectively removed from the measured signals. Additionally, eight different features are defined for the micro-defect motion-induced eddy current signals, and a feature dataset for various sizes of copper foil micro-defects is established under different motor speeds. The proposed IVY-CNN-BiLSTM-Attention model is then utilized for the quantification of micro-defect sizes, yielding excellent results.

Long-Term Outcomes of Ceramic Veneers Restorations: A Comprehensive Analysis of Clinical and Patient-Reported Metrics.

This study aimed to evaluate the clinical performance of ceramic veneers after 9-10 years, assess patient-reported outcomes, and examine their associations. Thirty patients with ceramic veneers aged 9-10 years were recalled for a clinical examination. Each participant completed a questionnaire on satisfaction and oral health-related quality of life, specifically using the Oral Impact on Daily Performance (OIDP) scale. The chi-square or Fisher exact test was used to assess the associations between tooth position and professional evaluations of veneer success and individual items. Consistency between professional evaluations of the veneers and patient-reported outcomes was evaluated using Weight Kappa. A total of 30 patients with 233 veneers participated in the study. Clinical evaluations deemed 9.87% (n = 23) of veneers as successful, 79.40% (n = 185) as surviving with complications, and 10.73% (n = 25) as a failure. The most common complications were marginal adaptation, gingival inflammation, and marginal discoloration. The participants were most frequently dissatisfied with the function of the veneers, food impaction, and cleaning difficulties. The OIDP assessment indicated that problems on daily activities such as cleaning, eating, and sleeping were predominantly affected. There were significant consistencies between certain clinical performance attributes and patient-reported outcomes. The survival rate of ceramic veneers was 89.3% after 9-10 years of follow-up. Among these, 79.4% had survived with complications such as material defects, secondary caries, and gingival problems. Given the discrepancies between patient-reported outcomes and clinical evaluations, further investigations into patient perception are needed alongside traditional clinical assessments. Educating patients about the potential complications associated with veneer restoration, especially specific to tooth location, is essential. Additionally, advising patients on proper oral hygiene practices is recommended to minimize the risk of gingival inflammation to enhance the longevity of the restoration.

A stochastic encoder using point defects in two-dimensional materials

While defects are undesirable for the reliability of electronic devices, particularly in scaled microelectronics, they have proven beneficial in numerous quantum and energy-harvesting applications. However, their potential for new computational paradigms, such as neuromorphic and brain-inspired computing, remains largely untapped. In this study, we harness defects in aggressively scaled field-effect transistors based on two-dimensional semiconductors to accelerate a stochastic inference engine that offers remarkable noise resilience. We use atomistic imaging, density functional theory calculations, device modeling, and low-temperature transport experiments to offer comprehensive insight into point defects in WSe2 FETs and their impact on random telegraph noise. We then use random telegraph noise to construct a stochastic encoder and demonstrate enhanced inference accuracy for noise-inflicted medical-MNIST images compared to a deterministic encoder, utilizing a pre-trained spiking neural network. Our investigation underscores the importance of leveraging intrinsic point defects in 2D materials as opportunities for neuromorphic computing.

Symmetry and Asymmetry in Mutations and Memory Retention during the Evolutionary Growth of Carbon Nanotubes.

Symmetry is a motif featured in almost all areas of science, and understanding the mechanism of symmetry breaking is challenging. Similar to mutations that disrupt symmetry in evolution, defects in materials offer insight into symmetry breaking. Here, we investigate symmetry in intragenerational mutations and symmetry breaking in transgenerational mutations in the evolutionary growth system of carbon nanotubes (CNTs). Mutations caused by pentagon-heptagon (5-7) pairs in different conformations shorten the lifespans of single-walled carbon nanotubes (SWNTs) by acting as time markers during growth. Symmetric distributions are observed for intragenerational mutations from (n, m) to (n + i, m-i) (where i ∈ caslon Z) with different appearance orders of pentagon and heptagon. Such symmetry breaks occur in transgenerational mutations. Intragenerational mutations occur multiple times on a SWNT, oscillating regularly between i and - i until termination occurs. These types and effects are retained in the form of memory to encode SWNTs during subsequent growth, resulting in a length reduction after each mutation. Our results provide a profound understanding of symmetry breaking and memory retention and offer guidance for the controlled synthesis of materials.

Local Remelting in Laser Powder Bed Fusion

In Laser Powder Bed Fusion, process material defects such as a lack of fusion, powder inclusions and cavities occur repeatedly by chance. These stochastically distributed defects can significantly reduce the mechanical performance of the components during operation. Possible in situ repair solutions such as multiple remelting of specific layer areas are promising approaches to avoid these defects in the finished component, thus improving the overall properties. In this context, the present study investigates the remelting of artificially introduced defects using the example of M789 tool steel. In the first step, the process parameter settings and mechanical properties were evaluated using a tensile test, and the density of the local repair was examined using X-ray computer tomography and a metallographic analysis. The results demonstrate that the mechanical properties of the tensile test are comparable with those of the reference samples while successfully increasing the component quality. This indicates that defects that arise during the process can be remelted without the loss of mechanical characteristics.

Advancing photocatalytic performance for enhanced visible-light-driven H2 evolution and Cr (VI) reduction of g-C3N4 through defect engineering via electron beam irradiation

Defect engineering has emerged as a strategic approach for optimizing photocatalyst band structures to improve performance. High-energy electron-beam irradiation is a promising method for inducing vacancies and defects in semiconductor materials, providing a rapid and efficient solution. In this study, we used simple electron-beam irradiation to modify g-C3N4 photocatalysts, resulting in the rapid formation of nitrogen vacancies within the material. Characterization techniques such as 13C-nuclear magnetic resonance, X-ray photoelectron spectroscopy, and electron paramagnetic resonance confirmed the formation of nitrogen vacancies, resulting in an asymmetric charge distribution within the g-C3N4 crystal structure. The optimized photocatalyst, gCN-300, exhibited remarkable performance, producing hydrogen at a rate of 750.57 ± 9.9 μmol g-1h−1 and reducing Cr (VI) by 83.5 % within 2 h with long-term photostability. The improved performance was attributed to increased visible-light absorption, a greater number of surface-active sites, improved electronic conductivity, and efficient charge transfer. These factors were supported by UV–visible diffuse reflectance spectroscopy, photoluminescence, and photoelectrochemical analysis. Thus, this study demonstrates the effectiveness of electron-beam irradiation in the large-scale production of defect-engineered photocatalysts with high performance.

Box-shaped necklace-like Fe3O4/MoS2-CNFs composite nanofibers with confinement effect enhancing sodium storage performance

A nanofiber necklace with a box-shaped structure has been designed. The box-shaped template can introduce other transition metal compounds, effectively compensating for the intrinsic defects of electrode materials. α-Fe2O3 nano cubes as the template mix with PAN to get α-Fe2O3/PAN. And then the α-Fe2O3 nano cubes calcine to form Fe3O4 with more valence state under high temperature, which get the box-shape necklace-like structure Fe3O4-CNFs. The confined space has formed by high concentrated HCl etching, the box-shaped necklace-like Fe3O4/MoS2 carbon nanofibers (BN Fe3O4/MoS2-CNFs) anode material is obtained by hydrothermal method of confining growth of MoS2 nanosheets. The mixed valence state of Fe3O4 (Fe3+/Fe2+) can effectively improve the conductivity of MoS2 nanosheets. The box-shape necklace structure constructed by α-Fe2O3 nanocube has mesoporous structure and confined space, which can alleviate the volume effect of electrode material. When the electrode material is applied to the SIBs, the reversible capacity is 354 mA h g−1 after 500 cycles at 0.5 A/g, and the specific capacity of 131.5 mA h g−1 can be maintained at 8 A/g. On the one hand, the heterogeneous structure in the confined space alleviates the volume effect of the material, thereby improving the long cycle performance, on the other hand, it effectively enhances the electrical conductivity and increases more lithium/sodium storage sites.

Inclusion-Based Model: Calculating Tooth Root Bending Strength Considering Steel Cleanliness

Current gear design guidelines and standards have given little or no consideration to the increase in strength that can be achieved by using ultra-clean steels. In order to fully exploit the potential of ultra-clean steels, it is therefore necessary to use higher-quality calculation methods that combine FEM stress calculations with local strength calculations. Therefore, the aim of this paper is to extend the inclusion-based model to allow for the calculation of the tooth root bending strength of gears with different steel cleanliness. For this purpose, a material analysis of 20MnCr5 with three different degrees of cleanliness is carried out and the respective material defect distribution is determined. In order to be able to represent the determined material defect distributions of the different cleanliness grades in the inclusion-based model, the model is extended accordingly, and a sensitivity analysis is carried out on the influence of the material defect distribution functions on the tooth root bending strength calculation. Finally, the model is applied to a pulsator test to verify the applicability of the model. The results of the verification show that the calculation of the mean bending strength of gears in pulsator investigations is generally possible with the extended inclusion-based model. The inclusion-based model thus offers the potential to improve the statistical significance of pulsator test results by supplementing the limited number of practical test points with the virtual test points of the inclusion-based model.

Vibrational and Magnetic States of Point Defects in Bilayer MoSe2.

Defects in two-dimensional materials profoundly impact the physicochemical properties of the systems, whose characterization is highly desirable at the atomic scale. Here, using spectroscopic imaging scanning tunneling microscopy, we elucidate the vibrational and magnetic states of MoSe antisite and VMo vacancy with different charge states embedded in ultrathin MoSe2 bilayers supported on graphene substrate. Stringent vibronic states with multimode coupling are resolved on the defects. The spectral intensities are tunable with the electron tunneling rates and well-reproduced by theoretical modeling. Moreover, first-principles calculations suggest that the defects host a local magnetic moment of 2 μB in their neutral state, which is directly confirmed by our spin-flip inelastic electron tunneling spectroscopy. Our study deepens the understanding of defect properties and paves the way of defect-engineering material functionalities and spin-catalytic applications.

Generative adversarial network-based ultrasonic full waveform inversion for high-density polyethylene structures

Accurate detection and characterization of defects are crucial to safety assurance of high-density polyethylene (HDPE) pipes used in the nuclear industry. Ultrasonic non-destructive evaluation (NDE) has the advantages of deep defect detection and high sensitivity, which can play a crucial role in the structural integrity of HDPE pipes. However, the quantitative reconstruction of high-contrast defects remains a significant challenge. To address this, a generative adversarial network based full waveform inversion (GAN-FWI) method is proposed for quantitatively reconstructing hidden defects in HDPE materials. This unsupervised learning method employs an acoustic wave equation based generator to optimize modeled data based on the feedback from a critic, which is used to differentiate between modeled data and measured data by adjusting network parameters. Compared to conventional full waveform inversion, the incorporation of physically constrained learning in the proposed GAN-FWI method can effectively alleviate the local minimum problem and aid in reconstructing high-contrast defects by reducing the sensitivity to the initial model and noise. Numerical and experimental results demonstrate the effectiveness of the proposed method in accurately and quantitatively reconstructing high-contrast defects in HDPE materials.

Comparison of Commercial REBCO Tapes Through Flux Pinning Energy

This work presents a comparison of different commercial tapes belonging to the second-generation High-Temperature Superconductors (2G HTS) produced by SuNAM Co., Ltd., SuperOx, and Shanghai Superconductors Technology Co., Ltd. (SST) companies. The aim is to investigate pinning mechanisms responsible for best performances, looking at the anisotropy of the irreversibility field and of the flux pinning energy. The irreversibility line states the upper limit of current-carrying capacity, whereas the flux pinning energy explores the ability of material defects to act as weak collectively or strong single vortex pinning centers. All investigated samples have artificial pinning centers (APCs) included in the superconducting matrix: BHO-doped EuBCO for SST, Y2O3 in YBCO for SuperOx, and Gd2O3 particles trapped in GdBCO for SuNAM. Resistive transition curves were measured in high magnetic fields up to 16 T for magnetic field orientations parallel and perpendicular to the tape surface. We found that the anistropy of SST tape shows an overall independence both on temperature and magnetic field, while the other two samples show a more complex behavior. This leads to the conclusion that properly engineered APC optimization in coated conductors can further reduce anisotropy of superconducting properties.

Divacancy graphene and graphyne as potential biomedical sensors: A first-principles study

Imbalance in the levels of hydrogen peroxide signals the onset of chronic diseases in our body. Thus, it becomes imperative to design simple material systems with single molecule sensitivity. In this study, we investigate graphene and graphyne based systems by including various vacancy structures to assess their potential as H2O2 sensor materials. From the computed adsorption energies, we select two promising candidates, interestingly both are divacancies in graphene and graphyne. We then investigate electron transport in both the systems using the DFT-NEGF approach. Upon adsorption of H2O2 on the 585 vacancy (a special case of divacancy) in graphene and divacancy graphyne monolayers, we observe distinguishing current-voltage and electron transmission characteristics, in particular, in the former. For the 585 divacancy graphene system through analysis of the electronic properties, we identify the underlying mechanisms responsible for the difference in current values between the clean and H2O2 adsorbed systems, and hence its sensing ability. This work opens up a new avenue of intelligently incorporating vacancy defects in cheaper carbon-based 2D materials to effectively serve as potentially highly sensitive and selective biosensors for early disease detection.

Laser ultrasonic inspection of surface and subsurface defects in materials based on deep learning

ABSTRACT Laser ultrasonic testing is an advanced non-destructive testing with high frequency and fast detection speed. However, most laser ultrasonic testing characterises defects by manually extracting the features of signals. To address this, deep learning is introduced to identify defects intelligently. Firstly, theoretical models based on finite elements are built to study the the surface and subsurface defects’ effects on surface acoustic wave (SAW) propagation. Experimental studies are also conducted on 5 samples with surface defects and 15 with subsurface defects. Then, Gaussian white noise is added to both simulation and experimental signals to expand the data set and imitate the complex conditions. The wavelet transform images of the reflected and transmitted signal are combined into a data set for training the convolutional neural network (CNN). The subsurface defects’ data set is similarly augmented. Finally, a total of 40,000 surface defect and 21,000 subsurface defect data are collected and split into training, test, and validation sets (6:2:2 ratio). The CNN accurately classifies surface defect penetration depth (minimum error 5.87%), while subsurface defect widths are determined with transfer learning (minimum error 5.6%). It proves that it is feasible to classify and identify defects by combining the laser ultrasonic method with deep learning.

Steady-State Response Analysis of an Uncertain Rotor Based on Chebyshev Orthogonal Polynomials

The performance of a rotor system is influenced by various design parameters that are neither precise nor constant. Uncertainties in rotor operation arise from factors such as assembly errors, material defects, and wear. To obtain more reliable analytical results, it is essential to consider these uncertainties when evaluating rotor performance. In this paper, the Chebyshev interval method is employed to quantify the uncertainty in the steady-state response of the rotor system. To address the challenges of high-dimensional integration, an innovative sparse-grid integration method is introduced and demonstrated using a rotor tester. The effects of support stiffness, mass imbalance, and uncertainties in the installation phase angle on the steady-state response of the rotor system are analyzed individually, along with a comprehensive assessment of their combined effects. When compared to the Monte Carlo simulation (MCS) method and the full tensor product grid (FTG) method, the proposed method requires only 68% of the computational cost associated with MCS, while maintaining calculation accuracy. Additionally, sparse-grid integration reduces the computational cost by approximately 95.87% compared to the FTG method.

Molecular dynamics assessment of primary damage efficiency for fusion relevant elements

Accurate computational modeling of fusion reactors depends upon precise figures of merit for radiation damage. The Norgett-Robinson-Torrens displacements per atom (NRT-dpa) model, a gold standard parameter for characterizing primary damage in solids, has been shown to overestimate surviving Frenkel defects in materials under high-energy irradiation. Here, we investigate the NRT-dpa model at higher primary knock-on atom (PKA) energies for six pure metals of interest in fusion. We compute updated defect production efficiency values and athermal recombination corrected (ARC)-dpa efficiency parameters for each element, which can be used for increased accuracy in neutronics simulations and fitting other extensions to the NRT-dpa.

Wear characteristics evolution of corundum wheel and its influence on performance in creep feed grinding of nickel-based superalloy

Wheel wear is an unavoidable occurrence in the process of creep feed grinding nickel-based superalloys, leading to reduced geometric accuracy, productivity, and surface integrity. Therefore, quantifying wheel wear forms and grinding performance is essential to minimize adverse impacts and optimize grinding processes. This study investigates the evolution of wheel wear and its consequences on grinding loads, chip formation, and surface quality. The results indicate that as the material removal volume (MRV) increases in the life cycle of the grinding wheel, the accumulation of timing damage leads to various forms of wear, mainly including grain fractures, material adhesion, attritious wear, and wheel clogging. The macro effects of the grinding ratio exhibit an initial increase followed by a subsequent decrease. At the micro level, the height of grain protrusions decrease, causing deviation from the normal distribution. Upon reaching the steady wear stage with an MRV of 9990 mm3, the wear flat rate and wheel clogging rate increase to approximately 10 % and 16.7 %, respectively. This indicates a significant rise in grinding thermo-mechanical load, serving as key indicators for evaluating the durability of grinding wheel. The presence of dull grains and material adhesion exacerbates the rubbing and ploughing effects, causing a transition of chip morphology from continuous flow to shear deformation, while heightened plastic pile-up contributes to elevated surface roughness. The intense thermo-mechanical coupling results in the formation of redeposited material and burn defects on the surface.

Impact of submicron Nb3Sn stoichiometric surface defects on high-field superconducting radiofrequency cavity performance

Nb3Sn film coatings have the potential to drastically improve the accelerating performance of Nb superconducting radiofrequency (SRF) cavities in next-generation linear particle accelerators. Unfortunately, persistent Nb3Sn stoichiometric material defects formed during fabrication limit the cryogenic operating temperature and accelerating gradient by nucleating magnetic vortices that lead to premature cavity quenching. The SRF community currently lacks a predictive model that can explain the impact of chemical and morphological properties of Nb3Sn defects on vortex nucleation and maximum accelerating gradients. Both experimental and theoretical studies of the material and superconducting properties of the first 100 nm of Nb3Sn surfaces are complicated by significant variations in the volume distribution and topography of stoichiometric defects. This work contains a coordinated experimental study with supporting simulations to identify how the observed chemical composition and morphology of certain Sn-rich and Sn-deficient surface defects can impact the SRF performance. Nb3Sn films were prepared with varying degrees of stoichiometric defects, and the film surface morphologies were characterized. Both Sn-rich and Sn-deficient regions were identified in these samples. For Sn-rich defects, we focus on elemental Sn islands that are partially embedded into the Nb3Sn film. Using finite element simulations of the time-dependent Ginzburg-Landau equations, we estimate vortex nucleation field thresholds at Sn islands of varying size, geometry, and embedment. We find that these islands can lead to significant SRF performance degradation that could not have been predicted from the ensemble stoichiometry alone. For Sn-deficient Nb3Sn surfaces, we experimentally identify a periodic nanoscale surface corrugation that likely forms because of extensive Sn loss from the surface. Simulation results show that the surface corrugations contribute to the already substantial drop in the vortex nucleation field of Sn-deficient Nb3Sn surfaces. This work provides a systematic approach for future studies to further detail the relationship between experimental Nb3Sn growth conditions, stoichiometric defects, geometry, and vortex nucleation. These findings have technical implications that will help guide improvements to Nb3Sn fabrication procedures. Our outlined experiment-informed theoretical methods can assist future studies in making additional key insights about Nb3Sn stoichiometric defects that will help build the next generation of SRF cavities and support related superconducting materials development efforts. Published by the American Physical Society 2024

A numerical investigation of the structural behavior of reinforced concrete beams fully or partially encased with UHPC layers in flexure

Weakness in the structural facilities appears due to design faults, poor construction, usage change, maintenance lack, material defects, and environmental conditions. Therefore, strengthening structures becomes necessary for deteriorated concrete and enhancing aging concrete elements to achieve satisfactory performance perfectly. This study numerically investigated the structural behavior of conventional concrete beams reinforced with Ultra-High Performance Concrete (UHPC) layers on different sides. The arrangement of UHPC layers, bonding method between traditional concrete and UHPC, layer thickness, and reinforcement ratio were examined using Abaqus software. The interface between the UHPC layer and conventional concrete sustains compressive, tensile, and shear stresses, which cause weak resistance to the stresses at the contact surfaces. Therefore, the simulation of contact face in Abaqus needs a proper numerical model relying on practical methods. However, using the bonding models available in Abaqus is crucial for reaching logical results comparable to the practical ones. The practical bonding approaches are highly consequential, such as roughening surfaces by sandblasting to attain a firm connection between the contact surfaces, which permits using a surface-based tie constraint for simulation. Further, epoxy resin conducts as a thin coating between contact surfaces, making it possible to simulate via a cohesive surface. The results proved that increasing the area encased by the UHPC layer improves load capacity further and reduces the deflection at the maximum load. Surrounding the beam on whole sides with a UHPC raises load capacity by 108 % and reduces deflection by 46 %. Adding a UHPC layer to the beam delays crack initiation and reduces their number and spacing. Increasing the UHPC layer thickness or reinforcement ratio enhances the load capacity of the beam at cracking and the ultimate state.