Search Results (548)

This study investigates the wear performance of 20CrMnTi steel, a commonly used material for spiral bevel gears, after heat treatment, with a focus on the microstructural evolution and wear behavior in both the surface and gradient direction of the carburized layer. The results show that the microstructure composition in the gradient direction of the carburized layer gradually transitions from martensite and residual austenite to a martensite–bainite mixed structure, and eventually transforms to fully bainitic in the matrix. With the extension of carburizing time, both the effective carburized layer depth and the hardened layer depth significantly increase. Wear track morphology analysis reveals that the wear track depth gradually becomes shallower and narrower, and the wear rate increases significantly with increasing load. However, the friction coefficient shows little sensitivity to changes in carburizing time and load. Further investigations show that as the carburized layer depth increases, the carbon concentration and hardness of the samples gradually decrease, resulting in an increase in the average wear rate and a progressive worsening of wear severity. After the wear tests, different depths of plowing grooves, spalling, and fish-scale-like features were observed in the wear regions. Additionally, with the increase in load and carburized layer depth, both the width and depth of the wear tracks significantly increased. The research results provide a theoretical basis for optimizing the surface carburizing process of 20CrMnTi steel and improving its wear resistance. Full article

This paper introduces a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm designed to enhance autonomous vehicle navigation on highways. The LSPP approach prioritizes smooth maneuvering, obstacle avoidance, passenger comfort, and adherence to road constraints, including lane boundaries, through optimized trajectory generation using quintic spline functions and a dynamic speed profile. Leveraging real-time data from the vehicle’s sensor fusion module, the LSPP algorithm accurately interprets the positions of surrounding vehicles and obstacles, creating a safe, dynamically feasible path that is relayed to the Model Predictive Control (MPC) track-following module for precise execution. The theoretical distinction of LSPP lies in its modular integration of: (1) a finite state machine (FSM)-based decision-making layer that selects maneuver-specific goal states (e.g., keep lane, change lane left/right); (2) quintic spline optimization to generate smooth, jerk-minimized, and kinematically consistent trajectories; (3) a multi-objective cost evaluation framework that ranks competing paths according to safety, comfort, and efficiency; and (4) a closed-loop MPC controller to ensure real-time trajectory execution with robustness. Extensive simulations conducted in diverse highway scenarios and traffic conditions demonstrate LSPP’s effectiveness in delivering smooth, safe, and computationally efficient trajectories. Results show consistent improvements in lane-keeping accuracy, collision avoidance, enhanced materials wear performance, and planning responsiveness compared to traditional path-planning methods. These findings confirm LSPP’s potential as a practical and high-performance solution for autonomous highway driving. Full article

Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems require high-precision accelerometers and proprietary software—investments often beyond the reach of municipally funded tram operators. To this end, as part of the research project “Accelerometer Measurements in Rail Passenger Transport Vehicles”, pilot measurement campaigns were conducted in Poland on tram lines in Gdańsk, Toruń, Bydgoszcz, and Olsztyn. Off-the-shelf smartphones equipped with MEMS accelerometers and GPS modules, running the Physics Toolbox Sensor Suite Pro app, were used. Although the research employs widely known methods, this paper addresses part of the gap in affordable real-time monitoring by demonstrating that, in the future, equipment equipped solely with consumer-grade MEMS accelerometers can deliver sufficiently accurate data in applications where high precision is not critical. This paper presents an analysis of a subset of results from the Gdańsk tram network. Lateral (x) and vertical (z) accelerations were recorded at three fixed points inside two tram models (Pesa 128NG Jazz Duo and Düwag N8C), while longitudinal accelerations were deliberately omitted at this stage due to their strong dependence on driver behavior. Raw data were exported as CSV files, processed and analyzed in R version 4.2.2, and then mapped spatially using ArcGIS cartograms. Vehicle speed was calculated both via the haversine formula—accounting for Earth’s curvature—and via a Cartesian approximation. Over the ~7 km route, both methods yielded virtually identical results, validating the simpler approach for short distances. Acceleration histograms approximated Gaussian distributions, with most values between 0.05 and 0.15 m/s², and extreme values approaching 1 m/s². The results demonstrate that low-cost mobile devices, after future calibration against certified accelerometers, can provide sufficiently rich data for ride-comfort assessment and show promise for cost-effective condition monitoring of both track and rolling stock. Future work will focus on optimizing the app’s data collection pipeline, refining standard-based analysis algorithms, and validating smartphone measurements against benchmark sensors. Full article

The vertical stiffness of railway tracks is crucial for ensuring safe and efficient rail transport. Rail-pad dynamic stiffness is a key component influencing track performance. Determining the dynamic stiffness of rail pads poses a challenge because it depends not only on the material and geometry of the rail pad but also on the testing conditions, due to the non-linear material response. To address this issue, a methodology is proposed in this paper to estimate dynamic stiffness using static stiffness measurements. This approach enables the prediction of dynamic stiffness for different situations from a single laboratory test. This study further examines whether this correlation remains valid for different types of rail pads, even when their mechanical behavior has been degraded by temperature, wear, or chemical agents. Experiments were conducted under varying temperatures and on rail pads that underwent mechanical and chemical degradation. The analysis assesses the validity of the static-to-dynamic stiffness correlation under degraded conditions and investigates the influence of each testing condition on the ability to estimate dynamic stiffness from static stiffness and operational parameters. The findings provide insights into the reliability of this predictive model and highlight the impact of degradation mechanisms on the dynamic behavior of rail pads. This research enhances the understanding of rail pad performance and offers a practical approach for evaluating dynamic stiffness. By considering all of the variables used in the analysis, the approach achieves R² values of up to 0.99, which carries significant implications for track design and maintenance. Full article

With the growing use of head-mounted displays (HMDs) such as smart glasses, text input remains a challenge, especially in mobile environments. Conventional methods like physical keyboards, voice recognition, and virtual keyboards each have limitations—physical keyboards lack portability, voice input has privacy concerns, and virtual keyboards struggle with accuracy due to a lack of tactile feedback. FlickPose is a novel text input system designed for smart glasses and mobile HMD users, integrating flick-based input and hand pose recognition. It features two key selection methods: the touch-panel method, where users tap a floating UI panel to select characters, and the raycast method, where users point a virtual ray from their wrist and confirm input via a pinch motion. FlickPose uses five left-hand poses to select characters. A machine learning model trained for hand pose recognition outperforms Random Forest and LightGBM models in accuracy and consistency. FlickPose was tested against the standard virtual keyboard of Meta Quest 3 in three tasks (hiragana, alphanumeric, and kanji input). Results showed that raycast had the lowest error rate, reducing unintended key presses; touch-panel had more deletions, likely due to misjudgments in key selection; and frequent HMD users preferred raycast, as it maintained input accuracy while allowing users to monitor their text. A key feature of FlickPose is adaptive tracking, which ensures the keyboard follows user movement. While further refinements in hand pose recognition are needed, the system provides an efficient, mobile-friendly alternative for HMD text input. Future research will explore real-world application compatibility and improve usability in dynamic environments. Full article

To elucidate the key material parameters governing the tribological performance of ceramic composites under dry sliding against steel, this study presents a comprehensive comparative assessment of the microstructural characteristics, mechanical performance, and tribological behavior of two alumina–zirconia (Al₂O₃–ZrO₂) ceramic composites, each reinforced with a 42 vol.% carbide phase: zirconium carbide (ZrC) and tungsten carbide (WC). Specifically, tungsten carbide (WC) was selected for its exceptional bulk mechanical properties, while zirconium carbide (ZrC) was chosen to contrast its potentially different interfacial reactivity against a steel counterface. ZrC and WC were selected as reinforcing phases due to their high hardness and distinct chemical and interfacial properties, which were expected to critically affect the wear and friction behavior of the composites under demanding conditions. Specimens were consolidated via spark plasma sintering (SPS). The investigation encompassed macro- and nanoscale hardness measurements (Vickers hardness HV₁, HV₁₀; nanoindentation hardness H), elastic modulus (E), fracture toughness (K_IC), coefficient of friction (COF), and specific wear rate (W_s) under unlubricated reciprocating sliding against 100Cr6 steel at normal loads of 10 N and 25 N. The Al₂O₃–ZrO₂–WC composite exhibited an ultrafine-grained microstructure and markedly enhanced mechanical properties (HV₁₀ ≈ 20.9 GPa; H ≈ 33.6 GPa; K_IC ≈ 4.7 MPa·m^½) relative to the coarse-grained Al₂O₃–ZrO₂–ZrC counterpart (HV₁₀ ≈ 16.6 GPa; H ≈ 27.0 GPa; K_IC ≈ 3.2 MPa·m^½). Paradoxically, the ZrC-reinforced composite demonstrated superior tribological performance, with a low and load-independent specific wear rate (W_s ≈ 1.2 × 10⁻⁹ mm³/Nm) and a stable steady-state COF of approximately 0.46. Conversely, the WC-reinforced system exhibited significantly elevated wear volumes—particularly under the 25 N regime—and a higher, more fluctuating COF. Scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM–EDX) of the wear tracks revealed the formation of a continuous, iron-enriched tribofilm on the ZrC composite, derived from counterface material transfer, whereas the WC composite surface displayed only sparse tribofilm development. These findings underscore that, in steel-paired tribological applications of Al₂O₃–ZrO₂–based composites, the efficacy of interfacial tribolayer generation can supersede intrinsic bulk mechanical attributes as the dominant factor governing wear resistance. Full article

This study discusses the frictional wear performance of three 3D-printed materials, acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), and high-impact polystyrene (HIPS), while evaluating different layer thickness levels. The materials were subjected to wear volume and rate tests by ball-on-disc wear tests at various thickness levels (0.1, 0.2, and 0.3 mm) and sliding distances. Lastly, SEM analysis was carried out to study the wear tracks and debris developed during the testing. Quantitatively, ABS maintained a mean wear volume below 0.15 mm³ across all test conditions (e.g., 0.05 ± 0.01 mm³ at 0.1 mm layer thickness and 150 m sliding distance), whereas PLA and HIPS recorded much higher averages of 1.5 mm³ and 3.0 mm³, respectively. With the increase in layer thickness, which caused an upward trend in the obtained results, the wear volume of the investigated materials also increased. ABS exhibited the smallest material loss of all three polymers; for example, at 0.1 mm layer thickness and a 150 m sliding distance, the mean wear volume was only 0.05 mm³, and even under the harshest condition tested (0.3 mm layer thickness, 300 m), the value remained below 0.15 mm³. PLA and HIPS showed higher wear volumes, while HIPS had the lowest resistance among the three materials. The multifunctional wear behavior difference contributed by material type was 59.76%, as shown through ANOVA, and that by layer thickness was 21.32%. Among the parameters investigated, material type had the largest control in wear behavior due to inherent variation in the structural characteristics of the material such as interlayer adhesion, toughness, and brittleness. For instance, the amorphous nature of ABS and its good layer adhesion provided significantly superior wear resistance compared to the brittle PLA and the poorly adhered HIPS. It is highlighted in this research that selecting appropriate material and layer thickness combinations can improve the durability of 3D-printed components. Full article

This study investigates the dimensional synthesis of steering mechanisms for front-wheel-drive, two-axle, four-wheeled vehicles using two metaheuristic optimization algorithms: Differential Evolution with golden ratio (DE-gr) and Improved Particle Swarm Optimization (IPSO). The vehicle under consideration has a track-to-wheelbase ratio of 0.5 and an inner wheel steering angle of 70 degrees. The mechanisms synthesized include the Ackermann steering mechanism and two variants (Type I and Type II) of the Watt-I six-bar steering mechanisms, also known as central-lever steering mechanisms. To ensure accurate steering and minimize tire wear during cornering, adherence to the Ackermann steering condition is enforced. The objective function combines the mean squared structural error at selected steering positions with a penalty term for violations of the Grashoff inequality constraint. Each optimization run involved 100 or 200 iterations, with numerical experiments repeated 100 times to ensure robustness. Kinematic simulations were conducted in ADAMS v2015 to visualize and validate the synthesized mechanisms. Performance was evaluated based on maximum structural error (steering accuracy) and mechanical advantage (transmission efficiency). The results indicate that the optimized Watt-I six-bar steering mechanisms outperform the Ackermann mechanism in terms of steering accuracy. Among the Watt-I variants, the Type II designs demonstrated superior performance and convergence precision compared to the Type I designs, as well as improved results compared to prior studies. Additionally, the optimal Type I-2 and Type II-2 mechanisms consist of two symmetric Grashof mechanisms, can be classified as non-Ackermann-like steering mechanisms. Both optimization methods proved easy to implement and showed reliable, efficient convergence. The DE-gr algorithm exhibited slightly superior overall performance, achieving optimal solutions in seven cases compared to four for the IPSO method. Full article

Wheel polygonal wear of metro deteriorates the vibration environment of the vehicle system, potentially leading to resonance-induced fatigue failure of components. This poses serious risks to operational safety and increases maintenance costs. To address the adverse effects of wheel polygonal wear, dynamic tracking tests and numerical simulations were conducted. The modal analysis focused on the vehicle–track coupling system, incorporating various track structures to explore the formation mechanisms and key influencing factors of polygonization. Test results revealed dominant polygonal wear patterns of the seventh to ninth order, inducing forced vibrations in the 50–70 Hz frequency range. These frequencies closely match the P2 resonance frequency generated by wheel–rail interaction. When vehicle–track coupling is considered, the track’s frequency response shows multiple peaks within this range, indicating susceptibility to resonance excitation. Additionally, rail joint irregularities act as geometric excitation sources that trigger polygonal development, while the P2 force resonance mode plays a critical role in its amplification. Full article

Quasicrystalline coatings based on Al₆₅Cu₂₀Fe₁₅ are of increasing interest as potential alternatives to conventional wear-resistant materials due to their unique structural and tribological properties. This study explores the influence of air pressure during high-velocity oxy-fuel (HVOF) spraying on the phase composition, morphology, and wear behavior of Al₆₅Cu₂₀Fe₁₅ coatings deposited on U8G tool steel. Coatings were applied at a fixed spraying distance of 350 mm using three air pressures (1.9, 2.1, and 2.3 bar), with constant propane (2.0 bar) and oxygen (2.1 bar) supply. X-ray diffraction analysis identified the formation of Al₇₈Cu₄₈Fe₁₄ and Al_0.5Fe_1.5 phases, while scanning electron microscopy revealed a dense, uniform microstructure with low porosity and homogeneous element distribution across all samples. Tribological testing using the ball-on-disk method showed wear track widths ranging from 853.47 to 952.50 µm, depending on the air pressure applied. These findings demonstrate that fine-tuning the air pressure during HVOF spraying significantly influences the structural characteristics and wear resistance of the resulting quasicrystalline coatings, highlighting their promise for advanced surface engineering applications. Full article

Epoxy Resin System (ERS) steel bridge pavement, which comprises a resin asphalt (RA) base layer and a modified asphalt wearing course, offers cost efficiency and rapid installation. However, the combined effects of traffic loads and environmental conditions pose significant challenges, requiring greater high-temperature stability than conventional pavements. The thermal sensitivity of resin materials and the use of conventional asphalt mixtures may weaken deformation resistance under elevated temperature conditions. This study investigates the thermal field distribution and high-temperature performance of ERS pavements under extreme conditions and explores temperature reduction strategies. A three-dimensional thermal field model developed using finite element analysis software analyzes interactions between the steel box girder and pavement layers. Based on simulation results, wheel tracking and dynamic creep tests confirm the superior performance of the RA05 mixture, with dynamic stability reaching 23,318 cycles/mm at 70 °C and a 2.1-fold improvement in rutting resistance in Stone Mastic Asphalt (SMA)-13 + RA05 composites. Model-driven optimization identifies that enhancing internal airflow within the steel box girder is possible without compromising its structural integrity. The cooling effect is particularly significant when the internal airflow aligns with ambient wind speeds (open-girder configuration). Surface peak temperatures can be reduced by up to 20 °C and high-temperature durations can be shortened by 3–7 h. Full article

Composite electrodeposited coatings hold significant potential for marine and aerospace applications due to their synergistic corrosion resistance and wear durability, yet nanoparticle agglomeration and interfacial incompatibility persistently undermine their performance. Conventional dispersion techniques—mechanical agitation, surfactants, or high-energy methods—fail to resolve these issues, often introducing residual stresses, organic impurities, or thermal damage to substrates. This study addresses these challenges through a novel thermal-assisted alkaline etching (TAE) protocol that synergistically removes surface oxides and enhances colloidal stability in β-SiC nanoparticles. By combining NaOH-based etching with low-temperature calcination (250 °C), the method achieves oxide-free SiC surfaces with elevated hydrophilicity and a ζ-potential of −25 mV, enabling submicron clustering (300 nm) without surfactants. Electrodeposited Co/SiC coatings incorporating TAE-SiC exhibited current-modulated reinforcement, achieving optimal SiC incorporation (5.9 at% Si) at 8 A/dm² through electrophoretic–hydraulic synergy, along with uniform cross-sectional distribution validated by SEM. Tribological assessments revealed shorter wear tracks in TAE-SiC-enhanced coatings compared to their untreated counterparts, suggesting enhanced interfacial coherence despite a comparable mass loss. Demonstrating scalability through cost-effective aqueous-phase chemistry, this methodology provides a generalized framework applicable to other ceramic-reinforced systems (e.g., Al₂O₃ and TiC), offering transformative potential for next-generation protective coatings in harsh operational environments. Full article

Ferritic stainless steels (FSSs) have attracted considerable attention due to their excellent corrosion resistance and significantly lower cost compared with nickel-bearing austenitic stainless steels. However, the high-temperature wear behavior of additively manufactured FSS 430 has not yet been thoroughly investigated. This study aims to examine the microstructural characteristics and wear properties of laser powder directed energy deposition (LP-DED) FSS 430 fabricated under varying laser powers and hatch distances. Wear testing was conducted at 25 °C and 300 °C after subjecting the samples to solution heat treating at 815 °C and 980 °C for 1 h, followed by forced fan cooling. For comparison, an AISI 430 commercial plate was also tested under the same test conditions. The microstructural evolution and worn surfaces were analyzed using SEM-EDS and EBSD techniques. The wear performance was evaluated based on the friction coefficients and cross-sectional profiles of wear tracks, including wear volume, maximum depth, and scar width. The average friction coefficients (AFCs) of the samples solution heat treated at 980 °C were higher than those treated at 815 °C. Additionally, the AFCs increased with hatch distance at both testing temperatures. A strong correlation was observed between Rockwell hardness and wear resistance, indicating that higher hardness generally results in improved wear performance. Full article

The geometry of the railway track affects rail wear significantly. If the rail wear can be predicted and considered during the alignment design phase, the problems it causes can be mitigated at the source by optimizing the values and combinations of railway track geometry parameters. However, the relationship between railway track geometry and rail wear remains unclear. It is hard to acquire rail wear data for different alignments with varying geometric parameters during the alignment design phase. This study develops a PSO-ANN model to establish the mapping relationship between railway track geometry and rail wear, enabling prediction of rail wear based on track geometry parameters. The model achieves prediction accuracies of 96.70% for inner rail wear and 98.13% for outer rail wear. Compared with the conventional ANN model, the PSO-ANN model reduces the prediction errors by 22.54% for inner rail wear and 55.69% for outer rail wear. Sobol sensitivity analysis is conducted to analyze the influence of the track geometry parameters on rail wear, revealing that inner rail wear is mainly affected by curve radius, transition curve length, and superelevation, while outer rail wear is predominantly influenced by curve radius. Full article

This article examines the modernization of railway systems with a focus on sustainable infrastructure development, aligning with the European Commission’s strategy for integrating 1520 mm gauge railways into the European 1435 mm gauge network. A key challenge lies in addressing the technical aspects of the railway infrastructure that are not explicitly detailed in the European strategy but have evolved through the parallel historical development of two distinct railway engineering systems. An analysis of calculation methodologies highlights that the primary difference in determining technical parameters for 1435 mm and 1520 mm tracks stems from the selection of the primary classifier based on functional purpose and strength requirements. Furthermore, the existing concept of mechanical system motion presents limitations in harmonizing the technical aspects of railway systems with different track gauges. To bridge this gap, two potential solutions are proposed. The first suggests expanding the conventional mechanical system motion framework by incorporating principles from the theory of relativity, while the second explores the application of elastic wave propagation theory as a novel conceptual model for railway system dynamics. The choice of modernization strategy will play a crucial role in ensuring long-term sustainability of the railway infrastructure, requiring a balanced approach that accounts for the operational intensity, infrastructure wear, and specific technical requirements of track elements in different railway gauge systems. Full article