Search Results (1,361)

Ti-6Al-4V alloy possesses excellent specific strength, corrosion resistance, and biocompatibility, rendering it widely applicable in aerospace, marine engineering and biomedical fields. Selective laser melting (SLM) serves as an effective technique for manufacturing complex Ti-6Al-4V components. However, SLM-formed specimens generally suffer from surface defects such as high surface roughness, adhered powders, spheroidized particles, and localized spatter, which degrade their service performance and limit further practical applications. Therefore, effective surface modification is urgently required. This work systematically explores the synergistic effects of sandblasting at various angles followed by acid pickling on the surface characteristics of SLM-formed Ti-6Al-4V alloy. The SLM Ti-6Al-4V samples were first treated by sandblasting at different impact angles and then subjected to acid pickling. Material mass loss, micro-morphology, surface roughness, contact angle, surface microhardness, abrasive-particle embedment and surface residual stress were measured and analyzed. The results show that sandblasting angle exerts a remarkable influence on material removal behavior, abrasive-particle embedment and near-surface mechanical response. Scanning electron microscopy (SEM) observations indicate that sandblasting at different angles can not only effectively eliminate surface-adhered powders, but also generate impact pits, cutting grooves, and ploughing marks whose morphologies vary with sandblasting angles. The subsequent acid pickling process further removes loose particles and sharp protrusions, and promotes the formation of microscale surface structures. Benefiting from the combined effects of mechanical sandblasting and chemical acid pickling, the alloy samples exhibit substantially reduced surface roughness and enhanced surface wettability. Meanwhile, sandblasting induces work hardening and thus increases surface microhardness and surface residual stress, while acid pickling regulates surface morphology and the state of the work-hardened layer to a certain degree. Overall, this study provides an economical, efficient, and industrially feasible composite surface modification approach to reduce surface roughness, enhance hydrophilicity, and tailor surface hardness of SLM Ti-6Al-4V alloy. Full article

Magnesium alloys are used or considered for lightweight structures and biodegradable implants, but high electrochemical activity, limited wear resistance, and localized corrosion still limit their service reliability. Plasma electrolytic oxidation (PEO), also called micro-arc oxidation (MAO), forms an adherent ceramic scaffold. Discharge channels, interconnected pores, thermal cracks, and a mechanically weak outer layer mean that the as-formed coating is rarely a complete protective system. This review examines advanced PEO/MAO-based composite coatings through a process–structure–function lens and develops an application-oriented design framework. The discussion covers PEO/MAO process-window control, electrolyte and particle engineering, sol–gel and polymer sealing, layered double hydroxide/inhibitor systems, self-healing reservoirs, superhydrophobic and slippery interfaces, Ca-P/hydroxyapatite and polymer biofunctionalization, and duplex coatings for wear, electrical, and thermal functions. Emphasis is placed on how these modules regulate defect connectivity, mass transport, interfacial stability, damage response, tribocorrosion, and biodegradation, as well as on the evidence needed to support each claimed function. The analysis indicates that coating performance is governed not by multilayer complexity alone, but by the compatibility among the ceramic scaffold, functional module, dominant failure mode, and service-specific validation protocol. Chloride-exposed structures require durable pore sealing and active inhibition; wear-critical components require coupled corrosion–wear assessment; and biodegradable implants require a degradation window that balances corrosion moderation, cytocompatibility, biofunctionality, and residual mechanical integrity. Remaining challenges include interfacial durability, finite inhibitor reservoirs, wetting-state instability, process reproducibility, scale-up, and life-cycle impacts. The proposed process maps and validation criteria are intended to support modular, testable, and application-specific PEO/MAO surface systems for magnesium alloys. Full article

Background: Gram-negative bacteria resistant to multiple drugs are a major cause of illness and death worldwide. Their remarkable capacity to develop resistance to antibiotics makes them a serious concern in medical practice. Methods: A simple, green, novel method is used to synthesize ZnO nanoparticles (ZnO NPs) using ethanolic extracts of Diplazium esculentum via precipitation. Results: ZnO NPs exhibit a hexagonal structure with a particle size of ~30 nm and a band gap of 3.24 eV. The defect sites formed in ZnO NPs were estimated using prominent peaks in the photoluminescence spectra. ZnO NPs displayed a more than 4-log reduction in multi-drug-resistant E. coli and K. pneumoniae clinical isolates at a 500 μg/mL concentration. Moreover, ZnO NPs significantly reduced the biofilm bacterial cell viability of clinical isolates of Gram-negative bacteria. Complete eradication of biofilms was achieved for drug-resistant E. coli clinical isolates using a combination of sub-MIC of gentamicin and 500 μg/mL ZnO NPs. Green-synthesized ZnO NPs did not induce oxidative stress in mice, as indicated by unchanged GST, GSH, and thiol levels across all the tested organs. ZnO NPs showed both antibacterial and antibiofilm efficacy against drug-resistant strains of E. coli, K. pneumoniae, and S. aureus and completely eradicated E. coli biofilm in combination with gentamicin. Conclusions: Our study focuses on the sustainable synthesis of biocompatible ZnO NPs for the treatment of infections caused by pathogens belonging to the high-priority ESKAPE group. Full article

Silk fibroin (SF) is a promising bio-compatible material for transient and bio-integrated memory devices; however, its relatively high leakage current and limited resistance state stability remain critical issues. In this study, Ag/SF–TiO₂/Pt bio-memristors were fabricated using SF–TiO₂ composite films with TiO₂ nanoparticle concentrations of 0, 0.25, 0.5, and 1.0 wt%. SEM analysis showed that TiO₂ incorporation increased particle aggregation while maintaining continuous film morphology. Optical analyses revealed that TiO₂ nanoparticles reduced the apparent optical gap, enhanced sub-bandgap absorption, and suppressed photoluminescence intensity, indicating the formation of defect- and trap-related states. Electrical measurements demonstrated that TiO₂ incorporation effectively reduced leakage current and stabilized the high-resistance state. The devices exhibited stable bipolar resistive switching within ±1 V, with enhanced I_on/I_off ratios of approximately 10⁴–10⁵ after TiO₂ addition. Endurance and retention measurements confirmed reliable switching over 100 cycles and stable resistance states up to 10⁴ s. Capacitance analysis further revealed resistance state-dependent charge storage behavior, with higher capacitance in the low-resistance state due to conductive filament formation and TiO₂-assisted interfacial polarization. These results indicate that TiO₂ nanoparticles effectively modulate charge trapping, leakage suppression, and memory stability in SF-based bio-memristors. Full article

The charged-lepton masses are free Yukawa-sector parameters in the Standard Model, whereas their measured pole-mass ratios display a highly structured hierarchy and satisfy the Koide relation to notable accuracy. This paper develops a conditional mathematical-physics proposal in which these dimensionless regularities arise from a charge-neutral parent carrier-defect architecture before effective Higgs–Yukawa read-out. The assumptions of the construction are stated explicitly as structural postulates and are separated from their derived consequences. The central rule assigns equal primitive weight to admissible internal sectors that are indistinguishable at the level where they first become exposed; protected sectors are removed before counting, and later refinements are conditional on previously selected sectors. Under this rule, the Koide relation follows as an equal-power theorem between the democratic parent component and the orthogonal branch-splitting component of the charged-lepton root-amplitude state. A minimal endpoint construction then yields a rapidly stabilizing charged tower for the electron–muon ratio. Because deeper charged terms are too small to remove the remaining residual, the framework assigns that residual to the continuation-dual neutral branch. The resulting neutral overlap gives a leading solar-angle target of 33.21 degrees and closes the electron–muon ratio at the present experimental precision; the Koide relation then fixes the corresponding tau ratios. The construction does not replace the Standard Model but is proposed as a selection rule for the boundary values of effective charged-lepton Yukawa parameters, with pole masses used because the claimed invariant is attached to completed asymptotic particle read-out. Running parameters, the absolute mass scale, and the full Pontecorvo–Maki–Nakagawa–Sakata (PMNS) matrix remain outside the present derivation. The proposal has explicit failure conditions: improved measurements can exclude the predicted tau ratios or solar-angle target, and the claimed conditional uniqueness fails if a different counting scheme satisfies the same postulates while producing different endpoint weights. Full article

With increasing rail traffic intensity, reliable detection of shallow subsurface rail damage is essential for operational safety. This critical narrative review evaluates non-destructive testing technologies relevant to defects whose active crack front or principal scattering zone lies within the upper approximately 0.5–10 mm of the rail, while treating the 10–15 mm range as a transition to deeper-defect verification. Magnetic flux leakage, magnetic particle inspection, visual inspection, eddy current testing, and conventional ultrasonic testing are first examined as screening or confirmatory comparators. The review then focuses on four ultrasonic surface-wave excitation routes—contact piezoelectric, active air-coupled, electromagnetic acoustic, and laser ultrasonic—and distinguishes source-specific laboratory capability from demonstrated field evidence. Because the cited studies use different defect geometries, rail conditions, sensor configurations, speeds, and decision criteria, their numerical values are reported as source-conditioned evidence rather than as a normalized ranking. An engineering decision matrix links defect depth and size, inspection speed, surface condition, and noise environment to a recommended screening–confirmation workflow. The synthesis identifies contact piezoelectric UT/PAUT as the most mature quantitative confirmation route, while EMAT, air-coupled UT, and laser UT retain method-specific advantages but require stronger natural-defect and in-service validation. Full article

In-situ alloying during laser powder bed fusion (LPBF) offers great compositional flexibility but is prone to process-induced defects. To address this problem, we developed a machine learning framework to predict and minimize major defects such as porosity (inclusive of lack of fusion, gas pores, and keyhole-induced porosity) and unmelted Nb particles (partially and completely unmelted particles) in LPBF-fabricated in-situ alloyed Ti–45Nb alloy. For this purpose, two independent least-squares boosting (LSBoost) ensemble regressors were trained using five process parameters (part shape, laser power, scan speed, hatch spacing, and scan rotation), along with their polynomial and interaction terms, to capture nonlinear relationships. Under a restricted 4-fold cross-validation, these models achieved pooled out-of-fold R² values of 0.672 for porosity and 0.702 for unmelted Nb, despite being trained on a small dataset. The grouped permutation importance analysis revealed that porosity is primarily governed by hatch spacing and laser power, whereas unmelted Nb particles are primarily governed by laser power and scan speed. The models were implemented in two graphical interfaces: a forward predictor for real-time defect estimation and an inverse optimizer for identifying low-defect parameter sets. Together, they establish a unified, data-driven approach for defect-aware process detection, prediction, and optimization in in-situ alloyed systems, offering a pathway towards reproducible, low-defect additive manufacturing. Full article

Self-assembled nanosphere structures are widely used as bottom-up platforms for ordered micro- and nanostructures, with applications in photonic crystals, sensing platforms, functional coatings, drug delivery, and nanosphere lithography. Their performance and reproducibility depend on structural order, packing density, interparticle spacing, and defect density and distribution. Thus, reliable imaging and quantitative defect detection are needed for quality evaluation and process optimization. This review provides an overview of defect characteristics, imaging systems, and defect detection algorithms for self-assembled nanosphere structures. It first introduces representative zero-, one-, two-, and three-dimensional assemblies, followed by a summary of common defects, including vacancies, interstitial particles, dislocations, grain boundaries, stacking faults, voids, and cracks. Optical microscopy, electron microscopy, atomic force microscopy, scanning near-field optical microscopy, and correlative techniques are compared in terms of resolution, field of view, temporal resolution, contrast mechanism, in situ capability, and compatibility with feedback control. Algorithmic approaches are also reviewed, encompassing classical image processing, machine learning, and deep learning, along with their applications in segmentation, localization, classification, and high-throughput analysis. Overall, reliable defect inspection requires integrated workflows. These workflows should combine appropriate imaging systems, image quality control, transferable algorithms, standardized datasets, and closed-loop feedback. Full article

Densely distributed small holes significantly affect the mechanical behavior and fracture characteristics of rock masses. In this study, the two-dimensional particle flow code was employed to establish a series of numerical models of dolomitic limestone, with the number of small circular holes increasing according to the sequence (2n − 1)² (n = 1–6). Comparative models with equivalent area and variable spacing were additionally designed to explore their regulatory effects. The strength, crack evolution, and contact force chain distribution of each model were systematically analyzed. The results reveal that the number of small holes exhibits an approximately linear negative correlation with the rock strength. The number of holes dominates the crack initiation location and propagation path. The crack initiation stress gradually decreases with increasing hole number, while the ratio of crack initiation stress to peak stress exhibits a V-shaped trend. As the hole number increases, the distribution of compressive force chains shifts from the sides of holes to the vertical strips between holes, and tensile force chains become significantly enhanced in the areas above and below the holes. Hole spacing and equivalent area exert only local modulating effects; the number and spatial arrangement of holes remain the dominant controls on strength deterioration and fracture evolution. These findings offer a theoretical foundation for stability assessment in rock masses characterized by densely distributed hole defects. Full article

Material extrusion (MEX), commonly known as fused deposition modeling (FDM), has become a widely adopted additive manufacturing (AM) technology owing to its low equipment cost and broad polymer compatibility. However, the geometric fidelity of the printed line often suffers from defects that compromise overall part quality. Specifically, residual edge non-uniformity degrades surface finish, while uncontrolled line width variability causes undesired gaps or overlaps that undermine mechanical performance. Therefore, ensuring an accurate line width and low edge non-uniformity is essential for advancing material extrusion toward high-precision industrial applications. In this study, a machine learning framework is proposed for the rapid prediction and analysis of printed line characteristics. Nozzle temperature, print speed, and material flow rate were considered as input process parameters. Mean line width and edge non-uniformity were taken as the target responses. Four representative machine learning algorithms (XGBoost, BPNN, GPR, and SVR) were adopted for model development. To enhance predictive accuracy, these models were optimized using Particle Swarm Optimization for automatic hyperparameter tuning. Subsequently, comparative evaluations identified GPR as the optimal predictive model. Furthermore, a SHAP-based interpretability analysis was conducted, revealing that nozzle temperature dominates line width, while the flow rate governs edge non-uniformity. Consequently, this interpretable and computationally efficient surrogate modeling approach provides a robust foundation for future closed-loop quality control and inverse process design. Full article

IN625-2NbC-2SiC composite coatings were successfully deposited on IN625 substrates using laser cladding technology. This study systematically explores the dependency of phase assemblage, microstructural characteristics, microhardness, and wear behavior on the applied laser power. Experimental results show that the phase composition of the coatings remains essentially unchanged across different power levels, primarily consisting of γ-(Ni, Cr), NbC, and SiC, with partial retention or reprecipitation of NbC particles. Under low laser power, local defects rich in Si and C appear in the coating, which is primarily attributed to insufficient melting or uneven dispersion of SiC particles. An optimal power of 1500 W results in a more homogeneous structure, better elemental distribution, and improved carbide dispersion. However, excessively high laser power may lead to excessive heat input, reduced cooling rate, and local microstructural inhomogeneity. Microhardness and tribological tests demonstrate that laser cladding significantly improves the surface properties of the IN625 substrate. The average microhardness values of the substrate, S1 to S4 are 250.5, 345.4, 357.2, 367.1, and 338.2 HV, respectively. Among them, the S3 coating exhibits the highest microhardness, which is approximately 46.5% higher than that of the substrate. Meanwhile, the S3 coating shows the lowest average friction coefficient and wear rate. The wear resistance ranking is as follows: S3 > S2 > S1 > S4 > substrate. The superior wear resistance of S3 is largely due to its high hardness, uniform structure, and well-distributed carbide reinforcements, which strengthen its resistance to deformation and abrasive wear. Based on overall consideration of phase, microstructure, and tribological performance, 1500 W is concluded to be the optimal laser power under the conditions investigated. Full article

Abrasive flow machining (AFM) is an effective finishing process for complex internal surfaces, particularly cavities, intersecting holes, and micro-channels that are difficult to access using conventional tools. However, when low-viscosity abrasive media is used (here defined, relative to conventional putty-like viscoelastic AFM carriers (with apparent viscosities of 10³–10⁵ mPa·s), as a water-based slurry with an apparent viscosity below 300 mPa·s over the operating shear-rate range), unfavorable flow conditions during the initial polishing stage can induce local over-polishing, erosion depressions, stepped patterns, and cavitation pits, resulting in non-uniform surface quality. The relationship between these flow behaviors and polishing defects remains insufficiently understood. To address this issue, this study investigates the AFM process applied to turbine blade film cooling holes through combined experimental and numerical approaches. The observed defects include erosion depressions, stepped surface patterns, and cavitation pits. The effects of abrasive injection pressure, flow velocity, hole geometry, abrasive viscosity, and particle size on defect formation are systematically examined. The results show that the initial abrasive filling level strongly affects defect distribution by altering the evolution of shear fields and void regions within the hole. Experimentally, at high Reynolds numbers (Re > 2 × 10⁴), intensified local shear and cavitation promote defect formation, while a moderate inclination angle (45–60°) and a higher aspect ratio (>8) are favorable for polishing uniformity. Complementary numerical simulations further indicate that smaller abrasive particles (<5 μm) and a moderate abrasive viscosity (~60 mPa·s) are predicted to improve polishing uniformity. This study clarifies the fluid-dynamic origin of polishing defects in film cooling holes and provides process guidance for suppressing local over-polishing, cavitation, and uneven material removal. Full article

As semiconductor design rules continue to shrink, random nanoscale particle contamination on non-patterned wafers has become a critical source of yield loss and process instability. This study presents a simulation-guided workflow for improving the practical detectability of 30 nm particles in an optical wafer inspection system without replacing the installed platform. Three controllable optical parameters—illumination polarization, wavelength, and incidence angle—were investigated through defect simulation and then validated on a production-relevant non-pattern inspection tool. The simulation and experimental results showed that p-polarized illumination generated stronger defect-relevant scattering contrast than S-polarization, 266 nm illumination provided the best practical detection performance among the evaluated wavelength conditions, and oblique illumination produced more favorable defect visibility than vertical incidence. Guided by these findings, an integrated inspection recipe using P-polarization, 266 nm illumination, and an oblique incidence angle of 20–25° was implemented on the inspection tool. Under the optimized condition, 30 nm particles were detected, whereas the legacy condition failed to provide equivalent sensitivity. The results demonstrate that defect simulation can be used as a practical engineering instrument for recipe screening, sensitivity enhancement, and faster deployment of inspection improvements in high-volume semiconductor manufacturing. Full article

In this article, an inverse mathematical model was developed to achieve fast and simultaneous estimation of thermophysical and geometric parameters for thermal barrier coating (TBC) systems in high-temperature environments with measurement errors. First, considering the convective and radiative heat transfer between the TBC system and the external environment, a one-dimensional unsteady conduction–radiation coupled heat transfer model was originally developed in high-temperature environments. Subsequently, this forward model was solved using the finite volume method (FVM), and the grid independence as well as the accuracy were validated. Thereafter, an accelerated inverse model, which adopts a principal component analysis (PCA)-optimized artificial neural network (ANN) to fit and substitute the forward model and employs the particle swarm optimization (PSO) algorithm for estimation, was developed based on the inverse method. Finally, under three different noise conditions, two situations were used to perform simultaneous estimation studies: a two-parameter case (top coat thermal conductivity and thermally grown oxide (TGO) layer thickness) and a three-parameter case (adding the thickness of an additional existing debonding defect layer). The results show that the PCA-optimized ANN-PSO-based accelerated inverse model is approximately 111–130 times faster than the traditional PSO-based inverse model, and the maximum relative errors of the arithmetic means of 20 estimations for two-parameter and three-parameter situations under three noise cases are on the order of 2.4% to 4.3%. Overall, the proposed accelerated inverse model achieves a favorable balance between speed and accuracy in multi-type parameter simultaneous estimation for TBC systems at high temperature, providing a methodological basis for addressing parameter estimation in more complex situations in future research. Full article

A nanostructured Ti_0.8V_0.2C solid solution carbide was successfully synthesized via high-energy mechanical alloying (MA) of elemental Ti, V, and C powders for 20 h in a planetary ball mill under argon atmosphere. Phase evolution and microstructural transformation were tracked using XRD, SEM/EDX, and TEM. Progressive alloying resulted in continuous refinement of the carbide structure, where the crystallite size was reduced to ~11–15 nm and the lattice microstrain increased up to 0.93 % after 20 h of MA. TEM observations confirmed the formation of highly dispersed nanocrystalline Ti_0.8V_0.2C solid-solution carbide particles with sizes of 15–20 nm. This work demonstrates the effectiveness of MA in generating a novel Ti–V-based nanocarbide solid solution and highlights the critical role of milling duration in tailoring structural refinement and defect accumulation at the nanoscale. Full article