Search Results (3,300)

In recent years, deep learning-based image classification has made significant progress, especially in safety-critical perception fields such as intelligent vehicles. Factors such as vibrations caused by NVH (noise, vibration, and harshness), sensor noise, and road surface roughness pose challenges to robustness and real-time deployment. The Transformer architecture has become a fundamental component of high-performance models. However, in complex visual environments, shifted window attention mechanisms exhibit inherent limitations: although computationally efficient, local window constraints impede cross-region semantic integration, while deep feature processing obstructs robust representation learning. To address these challenges, we propose DAR-Swin (Dual-Attention Revamped Swin Transformer), enhancing the framework through two complementary attention mechanisms. First, Scalable Self-Attention universally substitutes the standard Window-based Multi-head Self-Attention via sub-quadratic complexity operators. These operators decouple spatial positions from feature associations, enabling position-adaptive receptive fields for comprehensive contextual modeling. Second, Latent Proxy Attention integrated before the classification head adopts a learnable spatial proxy to integrate global semantic information into a fixed-size representation, while preserving relational semantics and achieving linear computational complexity through efficient proxy interactions. Extensive experiments demonstrate significant improvements over Swin Transformer Base, achieving 87.3% top-1 accuracy on CIFAR-100 (+1.5% absolute improvement) and 57.0% mAP on COCO2017 (+1.3% absolute improvement). These characteristics are particularly important for the active and passive safety features of intelligent vehicles. Full article

Mechanical forces, chemical and electrochemical reactions, and environmental variables can all lead to surface degradation of parts. Composite coatings can be applied to these materials to enhance their surface characteristics. Recently, nickel-based composite coatings have gained greater attention because of their remarkable wear resistance. The efficiency, precision, and affordability of this process make it a popular method. In addition, electroplating nickel-based composites offers a more environmentally friendly alternative to traditional dangerous coatings such as hard chrome. Tribological and wear characteristics are highly dependent on several variables, such as particle parameters, deposition energy, fluid dynamics, and bath composition. Mass loss, coefficient of friction, hardness, and roughness are quantitative properties that provide useful information for coating optimization and selection. Under optimized electrodeposition conditions, the Ni-SiC-graphite coatings achieved a 57% reduction in surface roughness (Ra), a 38% increase in microhardness (HV), and a 25% reduction in wear rate (Ws) compared to pure Ni coatings, demonstrating significant improvements in tribological performance. Overall, the incorporation of SiC nanoparticles was found to consistently improve microhardness while graphite or MoS₂ reduces friction. Differences in wear rate among studies appear to result from variations in current density, particle size, or test conditions. Furthermore, researchers run tribology studies and calculate the volume percentage using a variety of techniques, but they fall short in providing a sufficient description of the interface. This work primarily contributes to identifying gaps in tribological research. With this knowledge and a better understanding of electrodeposition parameters, researchers and engineers can improve the lifespan and performance of coatings by tailoring them to specific applications. Full article

Cr8 steel should be Steel containing ~8 wt.% of chromium is widely used in demanding die applications due to its excellent wear resistance; however, conventional shot peening, while enhancing strength, inevitably increases surface roughness, thereby compromising overall performance. To address this limitation, this study systematically investigates the influence of ultrasonic surface rolling (USR) step size—comparing 0.06 mm and 0.12 mm—on mitigating surface degradation and improving surface integrity. Friction wear and electrochemical corrosion tests demonstrate that USR effectively reduces surface roughness and enhances microhardness. The 0.06 mm step size achieves superior results, yielding the lowest surface roughness (0.8317 μm), highest microhardness (647.47 HV), lowest friction coefficient (0.655), and optimal corrosion resistance (minimum corrosion rate reduction: 3.472 µA·cm⁻², corresponding to an inhibition efficiency of 37.05%). These performance improvements are attributed to the synergistic effects of surface smoothing and work hardening, resulting from more uniform processing achieved under a smaller step size. Consequently, a 0.06 mm step size is determined to be optimal, establishing the integrated shot peening–USR process as a highly effective strategy for enhancing surface properties and extending the service life of critical Cr8 steel components in industrial applications. Full article

The TC4 alloy is widely used in aerospace and marine engineering due to its excellent mechanical properties and corrosion resistance. However, titanium alloys often face fretting wear problems during use, which affect their long-term stability and service life. This study investigates the effects of surface mechanical attrition treatment (SMAT) time on the surface morphology, microstructure, stress distribution, and fretting wear properties of TC4 alloy. Characterization was performed using white light interferometry, EBSD, SEM, XRD, and microhardness measurements. The results show that SMAT significantly changes the surface and wear properties of TC4 alloy. With the increase in SMAT time from 0 to 240 min, the surface roughness (Ra), hardness, deformation depth, and stress gradually increase while the grain size decreases. After 240 min of SMAT, the TC4 alloy exhibited optimal fretting wear resistance, achieving a wear depth of 14.27 μm, a wear volume of 2.48 × 10⁶ μm³, and a wear rate of 1.24 × 10³ μm³/s. This represents a significant improvement, corresponding to an approximate 32.8% reduction in wear depth and a ~48% reduction in both wear volume and wear rate compared to the untreated sample. Full article

Synthetic Aperture Radar (SAR) images are highly valuable for detecting water surfaces characterized by low roughness and minimal microwave reflection, which makes them essential for flood detection. Despite these advantages, SAR imagery still faces inherent challenges, particularly systematic noise, which limits the accuracy of pixel-level flood detection and causes fine-grained flood areas to be easily overlooked. To tackle these challenges, this study proposes a novel flood detection algorithm, the multi-frequency fuzzy uncertainty fusion network (MFF-Net), which is built upon a multi-scale architecture. Particularly, the multi-frequency feature extraction module in MFF-Net extracts frequency features at different levels, which mitigate systematic noise in the SAR images and improve the accuracy of pixel-level flood detection. The fuzzy uncertainty fusion module further mitigates noise interference and more effectively detects subtle flood areas that may be overlooked. The combined effect of these modules significantly enhances the detection capability for fine-grained flood areas. Experiments validate the effectiveness of MFF-Net on SAR benchmarks, including the MMflood Dataset with 50.2% of IoU, the Sen1Floods11 Dataset with 45.07% of IoU, the ETCI 2021 Dataset with 44.35% and the SAR Poyang Lake Water Body Sample Dataset with 57.27% of IoU, respectively. In addition, it has also been tested on actual flood events. Full article

The powder-mixed electric-discharge machining (PMEDM) process has been the focus of researchers for quite some time. This method overcomes the constraints of conventional machining, viz., low material removal rate (MRR) and high surface roughness (SR) in hard-cut materials, tool failure, and a high tool wear ratio (TWR). However, to determine the optimal machining parameter levels for improving MRR, surface finish must be measured during actual experimentation using various parameter levels across different materials. It is a very costly and time-consuming process for industries. However, in the age of Industry 4.0 and artificial intelligence machine learning (AI-ML), it provides an efficient solution to real manufacturing problems when big data is available. In this study, experimentation was conducted on AISI D2 steel using the PMEDM process for SR analysis with different parameters, viz. current, voltage, cycle time (T_On), powder concentration (PC), and duty factor (DF). Moreover, machine learning models were used to predict SR values for selected parameter levels in the PMEDM process. In this research, Gaussian process regression (GPR) with a squared exponential kernel, support vector machines, and ensemble regression models were used for computational analysis. The results of this work showed that Gaussian regression, support vector machine, and ensemble regression achieved 95%, 92%, and 83% accuracy, respectively. The GPR model achieved the best predictive performance among these three models. Full article

Laser powder bed fusion (LPBF) is the best suited technology to manufacture temperature-resistant Ti-6Al-2Sn-4Zr-6Mo parts with complex geometrical features for high-end applications. Improving printing accuracy by reducing the layer thickness (t) generally requires repeating a tedious and time-consuming process optimization routine. To simplify this endeavour, the present work proposes three process equivalence criteria allowing to transfer optimized process conditions from one printing parameter set to another. This approach recommends keeping the volumetric laser energy density (VED) and hatching space-to-layer thickness ratio (h/t) constant, while adjusting the scanning speed (v) and hatching space (h) accordingly. To validate this approach, Ti6246 parts were printed with 50 µm and 25 µm layer thicknesses, while keeping VED = 100 J/mm³ and h/t = 3 constant for both cases. The printed samples were analyzed in terms of their density, microstructure and mechanical properties, as well as the geometric compliance of wall-, gap- and channel-containing artefacts. Highly dense samples exhibiting comparable microstructures and mechanical properties were obtained with both parameters sets investigated. However, they induced markedly differing geometric characteristics. Notably, using 25 µm layers allowed printing walls as thin as 0.2 mm as compared to 1.0 mm for 50 µm layers. Full article

The cleanliness and functionalization of metal surfaces are critical factors to determining their performance in high-performance microelectronic packaging, reliable biomedical implants, advanced composite bonding, and other fields. Compared to traditional wet cleaning methods, plasma cleaning technology has emerged as a research hotspot in surface engineering due to its unique advantages, such as high efficiency and environmental friendliness. It operates under versatile conditions (e.g., power: tens of watts to several kilowatts; pressure: atmospheric to low vacuum; treatment time: seconds to minutes), enabling not only efficient contaminant removal but also targeted surface functionalization, including dramatically enhanced hydrophilicity (e.g., contact angles from >80° to <10°), significantly improved adhesion (e.g., up to 40% increase in bond strength), and modifications in surface roughness, corrosion resistance, and biocompatibility. This review systematically elaborates on the physical, chemical, and synergistic mechanisms of plasma cleaning technology as it acts on metal surfaces. It focuses on plasma cleaning applied to copper, aluminum, titanium and their respective alloys, as well as alloy steels, providing a detailed analysis of contaminant types, plasma cleaning methodologies, common challenges, surface functionalization responses, and subsequent functional applications. Furthermore, this review discusses the current challenges faced by plasma cleaning technology and offers perspectives on its future development directions. It aims to systematize the research progress in plasma cleaning of metal surfaces, thereby facilitating the transition of this technology towards large-scale industrial applications for metal surface functionalization. Full article

Titanium alloys are widely used for biomedical implants, but their performance is limited by wear, corrosion, and susceptibility to bacterial colonisation. To overcome these drawbacks, multilayer Ti–Cu oxide coatings were deposited on Ti6Al4V substrates using direct current magnetron sputtering. Two multilayer architectures (6 × 2 and 12 × 2 TiO₂/CuO bilayers) were fabricated and evaluated for their structural, mechanical, electrochemical, and biological properties. SEM/EDS and XRD confirmed well-adhered crystalline coatings consisting of rutile/anatase TiO₂ and monoclinic CuO with uniform elemental distribution. The coatings increased surface roughness, improved adhesion, and enhanced hardness by up to ~180% compared to uncoated Ti6Al4V alloy. Compared to the bare substrate, electrochemical testing in simulated body fluid showed higher corrosion resistance of both coated samples, but particularly for the 12 × 2 multilayers. Both architectures provided sustained Cu²⁺ release over seven days without a burst effect. In vitro biological testing showed that both multilayer coatings achieved over 96% inhibition of Gram-positive bacteria such as Staphylococcus aureus and Bacillus subtilis, while exhibiting moderate antibacterial effects against Gram-negative strains (Escherichia coli, Pseudomonas aeruginosa). Despite the presence of copper, MG-63 osteoblast-like cells demonstrated sustained viability and successful extracellular matrix mineralisation, indicating excellent cytocompatibility of the coatings with bone-forming cells. These results demonstrate that multilayer Ti–Cu oxide coatings can effectively balance antibacterial performance, corrosion resistance, mechanical strength, and support bone cell integration, making them a promising strategy for the surface modification of titanium-based biomedical implants. Full article

This work presents a comprehensive analysis of ball-end mill geometrical modification, with emphasis on surface quality and stability of the machining process. The study combines predictive modeling, analytical simulations, and experimental validation to evaluate the influence of cutting tool radius and process parameters on surface roughness. A composite factorial design of experiments was implemented to systematically investigate radius, stepover height, and inclination angle. Surface roughness was measured using a contact stylus profilometer, with secondary validation of selected samples by three-dimensional (3D) optical microscopy, ensuring robust verification of experimental outcomes. In addition, a two-dimensional (2D) computer-aided design (CAD)-based simulation model was developed to reconstruct toolpath overlaps and calculate roughness parameters for comparison. The predictive models were statistically compared with experimental and simulation results, showing consistent trends, while also highlighting deviations possibly due to process dynamics and cutting tool stability. Results indicate that ball-end mills with smaller radii demonstrate higher sensitivity to chatter and surface instability, while larger radii improve consistency as well as achievable roughness values. The combined methodology provides both practical and theoretical insights into optimizing cutting tool geometry for precision milling. The findings are relevant for cutting tool designers and manufacturing engineers seeking to balance productivity, cost, and surface integrity in finishing operations. Full article

Background/Objectives: This in vitro study examined the potential enhancement in resistance to accelerated aging in room-temperature vulcanized (RTV) maxillofacial silicone, intrinsically pigmented in two skin tones, through the use of zirconium oxide (ZrO₂) nanoparticles. Methods: A total of 128 disc-shaped specimens were created in rose silk and soft brown shades, each containing zirconium oxide concentrations of 0%, 1%, 2%, and 3% by weight. Color variation (ΔE*) was assessed initially and following 252, 750, and 1252 h of artificial aging, tested with a colorimeter. Surface roughness characteristics (R_a, R_q, R_t) were evaluated before and after 1252 h using atomic force microscopy (AFM). Structural, vibrational, and morphological characteristics were analyzed through X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and field emission scanning electron microscopy (FESEM). Results: Non-parametric tests (Friedman, Kruskal–Wallis, and Bonferroni-adjusted paired testing; p < 0.05) indicated that accelerated aging significantly increased ΔE* in all specimens. The addition of ZrO₂ reduced these changes; however, the optimal concentration differed by pigment: 1% for rose silk and 3% for soft brown. The effect on surface roughness depended on pigment type. Higher nanoparticle concentrations generally improved post-aging smoothness in soft brown samples, whereas rose silk showed a more variable response. XRD and FTIR analyses confirmed successful nanoparticle incorporation without altering the fundamental silicone structure, while FESEM demonstrated improved filler–matrix interaction in modified groups. Conclusions: Adjusting ZrO₂ concentration according to pigment type can improve the future color retention and surface characteristics of maxillofacial silicone. Full article

Antarctica’s aerodynamic roughness length (z_0m) is crucial for surface energy exchange and atmospheric modeling, but its remote sensing estimation remains challenging due to complex ice-surface conditions and limited observations. To address these challenges, this study establishes a z_0m retrieval framework derived from the Raupach model using Unmanned Aerial Vehicle (UAV), Reference Elevation Model of Antarctica (REMA), and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) datasets at three representative Antarctic sites. The results show that UAV benchmarks yield mean z_0m values of 0.009795, 0.011597, and 0.005203 m at Zhongshan Station, Great Wall Station, and Qinling Station, respectively. In experiments with ICESat-2 data, z_0m derived from ATL06 demonstrates accuracy comparable to that from ATL03 (RMSE = 7.45 × 10⁻⁶ m), with the best performance obtained at a 2 km window. Spatially, the agreement with UAV-derived z_0m decreases in the order: REMA > ICESat-2 (IDW-interpolated). The accuracy of REMA and ICESat-2 decreased with terrain complexity, from ice-free zones to the ice-shelf front and finally to the steep ice sheet margin. The elevation and slope variations emerge as dominant controls of z_0m spatial patterns. This study demonstrates the complementary strengths of UAV, REMA, and ICESat-2 datasets in Antarctic aerodynamic roughness estimation, providing practical guidance for data selection and methodology optimization. This study develops an improved z_0m retrieval method for Antarctica, clarifies the applicability and limitations of UAV, REMA, and ICESat-2 data, and provides methodological and data support for simulations of near-surface atmospheric parameters in Antarctica region. Full article

The m-lines spectroscopy is a precise, non-destructive and contactless method, and one of its main applications is the determination of the geometric-optical parameters of a thin film deposited over a substrate, namely the refractive index and the thickness of the film under analysis. The method was first described in 1969 with the seminal work of Tien, more than half a century ago, and, since then, it has been reported in the literature that at least two modal indices of the same polarization are required to unequivocally determine a given film’s refractive index and thickness. This constraint imposes a limit on the waveguide’s thickness, for it leaves out the possibility of determining the geometric-optical parameters of all films where only single-mode propagation is feasible. In this work, we propose and validate a strategy that extends the applicability of the method to single-mode operation, enlarging its operational thickness detection range. Moreover, the results obtained demonstrate that restricting the parameter extraction to fundamental modes leads to a measurable increase in precision. This improvement is attributed to the lower susceptibility to experimental uncertainties, lower sensitivity to surface roughness and nearby structures, and higher confinement that characterize fundamental modes as opposed to higher-order ones. Full article

This in vitro study investigated the shear bond strength (SBS) between titanium abutments and resin-based CAD/CAM restorative materials fabricated using additive (3D printing) and subtractive (milling) methods. The aim was to assess how different surface treatments—primer only, phosphoric acid etching with primer, and sandblasting with primer—affect bonding performance. A total of 120 cylindrical specimens were prepared using four CAD/CAM materials and bonded to titanium disks using dual-cure resin cement. SBS was measured following ISO 10477:2020 guidelines, and surface morphology was analyzed via scanning electron microscopy (SEM). Two-way ANOVA revealed that both the material type and surface treatment had statistically significant effects on SBS (p < 0.001), with a notable interaction between them. Additively manufactured materials exhibited higher SBS values compared to subtractive ones. The highest bond strength was observed in the sandblasted Saremco Crowntec group, while the lowest was in the primer-only Cerasmart group. SEM images confirmed enhanced surface roughness in sandblasted specimens, and failure mode analysis showed more cohesive and mixed failures in mechanically treated groups. These findings underscore the importance of selecting appropriate surface conditioning protocols tailored to each material type to improve bonding effectiveness in implant-supported restorations. Full article

The efficiencies of additive manufacturing (AM) over conventional processes have enabled the rapid production of aluminum (Al) alloys with AM. Because laser beam powder bed fusion (LB-PBF) parts do not offer the surface quality and geometrical accuracy for direct use, the functional surfaces of LB-PBF parts are usually machined by subtractive machining. The machinability of LB-PBF AlSi10Mg was studied in dry, MQL (used corn oil), and cryo-LN₂ cutting environments across distinct speed–feed combinations using CVD-AlTiN-coated carbide inserts, and surface integrity and tool life were quantified in terms of surface roughness (Ra) and flank wear (Vb), respectively. The lowest Ra (0.98–1.107 μm) was obtained with cryo-LN₂, followed by MQL and dry cutting environments, because the trends observed were consistent with the surface mechanisms observed in 3D topography and bearing curves. Similarly, the tool wear results mirrored the Ra results, lowest with LN₂ (0.087–0.110 mm), due to improved thermal management, reduced adhesion and abrasion, and shorter contact length. Cryo-LN₂ provided the best surface finish and tool life among all tested environments. To enable data-driven prediction, the limited dataset was augmented using SMOTE, and machine learning (ML) models were trained to predict Ra and Vb. CatBoost was found to yield the best Ra predictions (R² = 0.9090), while Random Forest and XGBoost yielded the best Vb predictions (R² ≈ 0.878). Full article