Search Results (890)

Background: Attachments are essential components in clear aligner therapy, enhancing retention and improving the predictability of tooth movements. Mechanical and wear properties of the composite resins used for attachment reproduction are critical to maintaining their integrity and shape over time. This study aimed to evaluate and compare the mechanical properties, thermal behavior, and wear performance of the hybrid composite Aligner Connect (AC) and the flowable resin (Connect Flow, CF). Methods: Twenty samples (ten AC and ten CF) were reproduced. All specimens underwent differential scanning calorimetry (DSC), combustion analysis, flat instrumented indentation, compression stress relaxation tests, and tribological analysis. A 3D wear profile reconstruction was performed to assess wear surfaces. Results: DSC and combustion analyses revealed distinct thermal transitions, with CF showing significantly lower Tg values (103.8 °C/81.4 °C) than AC (110.8 °C/89.6 °C) and lower residual mass after combustion (23% vs. 61%), reflecting reduced filler content and greater polymer mobility. AC exhibited superior mechanical properties, with higher maximum load (585.9 ± 22.36 N) and elastic modulus (231.5 ± 9.1 MPa) than CF (290.2 ± 5.52 N; 156 ± 10.5 MPa). Stress relaxation decrease was less pronounced in AC (18 ± 4%) than in CF (20 ± 4%). AC also showed a significantly higher friction coefficient (0.62 ± 0.060) than CF (0.55 ± 0.095), along with greater wear volume (0.012 ± 0.0055 mm³ vs. 0.0070 ± 0.0083 mm³) and maximum depth (36.88 ± 3.642 µm vs. 17.91 ± 3.387 µm). Surface roughness before wear was higher for AC (Ra, 0.577 ± 0.035 µm; Rt, 4.369 ± 0.521 µm) than for CF (Ra, 0.337 ± 0.070 µm; Rt, 2.862 ± 0.549 µm). After wear tests, roughness values converged (Ra, 0.247 ± 0.036 µm for AC; Ra, 0.236 ± 0.019 µm for CF) indicating smoothened and similar surfaces for both composites. Conclusions: The hybrid nanocomposite demonstrated greater properties in terms of stiffness, load-bearing capacity, and structural integrity when compared with flowable resin. Its use may ensure more durable attachment integrity and improved aligner–tooth interface performance over time. Full article

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

The study is motivated by the application of dry finish milling for post-build processing of additive Ti6Al4V blanks, since the use of neither lubricant nor coolants has been attracting increasing attention due to its environmental benefits, non-toxicity, and the elimination of the need for additional cleaning processes. For end mills, wear patterns were investigated upon finish milling of the SLM Ti6Al4V samples under various machining conditions (by varying the values of radial depth of cut and feed values at a constant level of axial depth of cut and cutting speed). When using all the applied milling modes, the identical tool wear mechanism was revealed. Built-up edges mainly developed on the leading surfaces, increasing the surface roughness on the SLM Ti6Al4V samples but protecting the cutting edges. However, abrasive wear was mainly characteristic of the flank surfaces that accelerated peeling of the protective coatings and increased wear of the end mills. The following milling parameters have been established as being close to rational ones: Vc = 60 m/min, Vf = 400 mm/min, a_p = 4 mm, and a_e = 0.4 mm. They affected the surface roughness of the SLM Ti6Al4V samples in the following way: max cutting thickness—8 μm; built-up edge at rake surface—50 ± 3 μm; max wear of flank surface—15 ± 1 μm; maximum adherence of workpiece. Mode III provided the maximum MRR value and negligible wear of the end mill, but its main disadvantage was the high average surface roughness on the SLM Ti6Al4V sample. Mode II was characterized by both the lowest average surface roughness and the lowest wear of the end mill, as well as an insufficient MRR value. Since these two modes differed only in their feed rates, their values should be optimized in the range from 200 to 400 mm/min. Full article

Modernizing irrigation systems (ISs) from traditional gravity methods to sprinkler and drip technologies has significantly improved water use efficiency. However, it has simultaneously increased electricity demand and operational costs. Integrating photovoltaic generators into ISs represents a promising solution, as solar energy availability typically aligns with peak irrigation periods. Despite this potential, photovoltaic pumping systems (PVPSs) often face reliability issues due to fluctuations in solar irradiance, resulting in frequent start/stop cycles and premature equipment wear. The IEC 62253 standard establishes procedures for evaluating PVPS performance but primarily addresses steady-state conditions, neglecting transient regimes. As the main contribution, the current paper proposes a non-intrusive, high-resolution monitoring system and a methodology to assess the performance of an isolated, high-power PVPS, considering also transient regimes. The system records critical electrical, hydraulic and environmental parameters every second, enabling in-depth analysis under various weather conditions. Two performance indicators, pumped volume efficiency and equivalent operating time, were used to evaluate the system’s performance. The results indicate that near-optimal performance is only achievable under clear sky conditions. Under the appearance of clouds, control strategies designed to protect the system reduce overall efficiency. The proposed methodology enables detailed performance diagnostics and supports the development of more robust PVPSs. Full article

To address the wear life prediction challenge of Guide Cones in passive compliant connectors under dynamic loads within specialized equipment, this study proposes a dynamic wear modeling and life assessment method based on the improved Archard model. Through integrated theoretical modeling, finite element simulation, and experimental validation, we establish a bidirectional coupling framework analyzing dynamic contact mechanics and wear evolution. By developing phased contact state identification criteria and geometric constraints, a transient load calculation model is established, revealing dynamic load characteristics with peak contact forces reaching 206.34 N. A dynamic contact stress integration algorithm is proposed by combining Archard’s theory with ABAQUS finite element simulation and ALE adaptive meshing technology, enabling real-time iterative updates of wear morphology and contact stress. This approach constructs an exponential model correlating cumulative wear depth with docking cycles (R² = 0.997). Prototype experiments demonstrate a mean absolute percentage error (MAPE) of 14.6% between simulated and measured wear depths, confirming model validity. With a critical wear threshold of 0.8 mm, the predicted service life reaches 45,270 cycles, meeting 50-year operational requirements (safety margin: 50.9%). This research provides theoretical frameworks and engineering guidelines for wear-resistant design, material selection, and life evaluation in high-reliability automatic docking systems. Full article

This study explores the machinability of Incoloy 800HT (high temperature) under a sustainable lubrication approach, employing a twin-nozzle minimum quantity lubrication (MQL) system with groundnut oil as a green cutting fluid. The evaluation focuses on key performance indicators, including surface roughness, tool flank wear, power consumption, carbon emissions, and chip morphology. Groundnut oil, a biodegradable and nontoxic lubricant, was chosen to enhance environmental compatibility while maintaining effective cutting performance. The Taguchi L₁₆ orthogonal array (three factors and four levels) was utilized to conduct experimental trials to analyze machining characteristics. The best surface quality (surface roughness, Ra = 0.514 µm) was obtained at the lowest depth of cut (0.2 mm), modest feed (0.1 mm/rev), and moderate cutting speed (160 m/min). The higher ranges of flank wear are found under higher cutting speed conditions (320 and 240 m/min), while lower wear values (<0.09 mm) were observed under lower speed conditions (80 and 160 m/min). An entropy-integrated multi-response optimization using the MOORA (multi-objective optimization based on ratio analysis) method was employed to identify optimal machining parameters, considering the trade-offs among multiple conflicting objectives. The entropy method was used to assign weights to each response. The obtained optimal conditions are as follows: cutting speed = 160 m/min, feed = 0.1 mm/rev, and depth of cut = 0.2 mm. Optimized outcomes suggest that this green machining strategy offers a viable alternative for sustainable manufacturing of difficult-to-machine alloys like Incoloy 800 HT. Full article

Friction leads to substantial energy losses and wear in mechanical systems. This study explores the tribological potential of the high-temperature superconductor precursor Y₂BaCuO₅ (Y211), synthesized via chemical co-precipitation, as a novel additive to PAO6 base oil. A 0.3 wt.% Y211/PAO6 lubricant (CD) was formulated using ultrasonic dispersion. Tribological performance was evaluated using a custom end-face tribometer (steel-on-iron) under varying loads (100–500 N) and speeds (300–500 rpm), comparing CD to neat PAO6. The results indicate that the Y211 additive consistently reduced the coefficient of friction (COF) relative to neat PAO6, maintaining a stable value around ~0.1. However, its effectiveness was strongly load-dependent: a significant friction reduction was observed at 100 N, while the benefit diminished at higher loads (>200 N), with the COF peaking around 200 N. Rotational speed exerted minimal influence. Compared with neat PAO6, the inclusion of 0.3 wt.% Y211 resulted in a reduction in the coefficient of friction by approximately 50% under low-load conditions (100 N), with COF values decreasing from 0.1 to 0.045. Wear depth measurements also revealed a reduction of over 30%, supporting the additive’s anti-wear efficacy. Y211 demonstrates potential as a friction-reducing additive, particularly under low loads, but its high-load performance limitations warrant further optimization and mechanistic studies. This highlights a novel tribological application for Y211. The objective of this study is to evaluate the tribological effectiveness of Y₂BaCuO₅ (Y211) as a lubricant additive, investigate its load-dependent friction behavior, and explore its feasibility as a multifunctional additive leveraging its superconductive precursor structure. Full article

Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person’s cooperation. The general face-based person recognition system often fails to determine the offender’s identity when they conceal their face by wearing helmets and masks to evade identification. In such cases, gait-based recognition is ideal for identifying offenders, and most existing work leverages a deep learning (DL) model. However, a single model often fails to capture a comprehensive selection of refined patterns in input data when external factors are present, such as variation in viewing angle, clothing, and carrying conditions. In response to this, this paper introduces a fusion-based multi-model gait recognition framework that leverages the potential of convolutional neural networks (CNNs) and a vision transformer (ViT) in an ensemble manner to enhance gait recognition performance. Here, CNNs capture spatiotemporal features, and ViT features multiple attention layers that focus on a particular region of the gait image. The first step in this framework is to obtain the Gait Energy Image (GEI) by averaging a height-normalized gait silhouette sequence over a gait cycle, which can handle the left–right gait symmetry of the gait. After that, the GEI image is fed through multiple pre-trained models and fine-tuned precisely to extract the depth spatiotemporal feature. Later, three separate fusion strategies are conducted, and the first one is decision-level fusion (DLF), which takes each model’s decision and employs majority voting for the final decision. The second is feature-level fusion (FLF), which combines the features from individual models through pointwise addition before performing gait recognition. Finally, a hybrid fusion combines DLF and FLF for gait recognition. The performance of the multi-model fusion-based framework was evaluated on three publicly available gait databases: CASIA-B, OU-ISIR D, and the OU-ISIR Large Population dataset. The experimental results demonstrate that the fusion-enhanced framework achieves superior performance. Full article

Tool wear modeling in CNC turning processes is critical for proactive maintenance and process optimization in intelligent manufacturing. However, traditional physics-based models lack adaptability, while machine learning approaches are often limited by poor interpretability. This study develops Kolmogorov–Arnold Networks (KANs) to address the trade-off between accuracy and interpretability in lathe tool wear modeling. Three KAN variants (KAN-A, KAN-B, and KAN-C) with varying complexities are proposed, using feed rate, depth of cut, and cutting speed as input variables to model flank wear. The proposed KAN-based framework generates interpretable mathematical expressions for tool wear, enabling transparent decision-making. To evaluate the performance of KANs, this research systematically compares prediction errors, topological evolutions, and mathematical interpretations of derived symbolic formulas. For benchmarking purposes, MLP-A, MLP-B, and MLP-C models are developed based on the architectures of their KAN counterparts. A comparative analysis between KAN and MLP frameworks is conducted to assess differences in modeling performance, with particular focus on the impact of network depth, width, and parameter configurations. Theoretical analyses, grounded in the Kolmogorov–Arnold representation theorem and Cybenko’s theorem, explain KANs’ ability to approximate complex functions with fewer nodes. The experimental results demonstrate that KANs exhibit two key advantages: (1) superior accuracy with fewer parameters compared to traditional MLPs, and (2) the ability to generate white-box mathematical expressions. Thus, this work bridges the gap between empirical models and black-box machine learning in manufacturing applications. KANs uniquely combine the adaptability of data-driven methods with the interpretability of physics-based models, offering actionable insights for researchers and practitioners. Full article

The vibration and noise of friction pairs are severe when cutting titanium alloy with cemented carbide tools, and the surface micro-texture can significantly reduce noise and friction. Therefore, it is very important to clarify the correlation mechanism between friction noise and friction force for processing quality control. Consequently, investigating the underlying mechanisms that link friction noise and friction is of considerable importance. This study focuses on the friction and wear acoustic signals generated by micro-textured cemented carbide–titanium alloy. A friction testing platform specifically designed for the micro-textured cemented carbide grinding of titanium alloy has been established. Acoustic sensors are employed to capture the acoustic signals, while ultra-depth-of-field microscopy and scanning electron microscopy are utilized for surface analysis. A novel approach utilizing the dung beetle algorithm (DBO) is proposed to optimize the parameters of variational mode decomposition (VMD), which is subsequently combined with wavelet packet threshold denoising (WPT) to enhance the quality of the original signal. Continuous wavelet transform (CWT) is applied for time–frequency analysis, facilitating a discussion on the underlying mechanisms of micro-texture. Additionally, features are extracted from the time domain, frequency domain, wavelet packet, and entropy. The Relief-F algorithm is employed to identify 19 significant features, leading to the development of a hybrid model that integrates Bayesian optimization (BO) and Transformer-LSTM for predicting friction. Experimental results indicate that the model achieves an R² value of 0.9835, a root mean square error (RMSE) of 0.2271, a mean absolute error (MAE) of 0.1880, and a mean bias error (MBE) of 0.1410 on the test dataset. The predictive performance and stability of this model are markedly superior to those of the BO-LSTM, LSTM–Attention, and CNN–LSTM–Attention models. This research presents a robust methodology for predicting friction in the context of friction and wear of cemented carbide–titanium alloys. Full article

Water-lubricated bearings (WLB), due to their pollution-free nature and low noise, are increasingly becoming critical components in aerospace, marine applications, high-speed railway transportation, precision machine tools, etc. However, in practice, water-lubricated bearings suffer severe friction and wear due to low-viscosity water, harsh conditions, and contaminants like sediment, which can compromise the lubricating film and shorten their lifespan. The implementation of micro-textures has been demonstrated to improve the tribological performance of water-lubricated bearings to a certain extent, leading to their widespread adoption for enhancing the frictional dynamics of sliding bearings. The shape, dimensions (including length, width, and depth), and distribution of these micro-textures have a significant influence on the frictional performance. Therefore, this study aims to explore the coupling effect of different micro-texture shapes and distributions on the frictional performance of water-lubricated sliding, using the computational fluid dynamics (CFD) analysis. The results indicate that strategically arranging textures across multiple regions can enhance the performance of the bearing. Specifically, placing linear groove textures in the outlet of the divergent zone and triangular textures in the divergent zone body maximize improvements in the load-carrying capacity and frictional performance. This specific configuration increases the load-carrying capacity by 7.3% and reduces the friction coefficient by 8.6%. Overall, this study provided critical theoretical and technical insights for the optimization of WLB, contributing to the advancement of clean energy technologies and the extension of critical bearing service life. Full article

In the coal industry, miner safety is increasingly challenged by growing mining depths and complex environments. The failure to wear Personal Protective Equipment (PPE) is a frequent issue in accidents, threatening lives and reducing operational efficiency. Additionally, existing PPE datasets are inadequate for model training due to their small size, lack of diversity, and poor labeling. Current methods often struggle with the complexity of multi-scenario and multi-type PPE detection, especially under varying environmental conditions and with limited training data. In this paper, we propose a novel minersPPE dataset and an improved algorithm based on YOLOv8, enhanced with Dilated-CBAM (Dilated Convolutional Block Attention Module) and DBB (Diverse Branch Block) Detection Block (YOLOv8-DCDB), to address these challenges. The minersPPE dataset constructed in this paper includes 14 categories of protective equipment needed for various body parts of miners. To improve detection performance under complex lighting conditions and with varying PPE features, the algorithm incorporates the Dilated-CBAM module. Additionally, a multi-branch structured detection head is employed to effectively capture multi-scale features, especially enhancing the detection of small targets. To mitigate the class imbalance issue caused by the long-tail distribution in the dataset, we adopt a K-fold cross-validation strategy, optimizing the detection results. Compared to standard YOLOv8-based models, experiments on the minersPPE dataset demonstrate an 18.9% improvement in detection precision, verifying the effectiveness of the proposed YOLOv8-DCDB model in multi-scenario, multi-type PPE detection tasks. Full article

This study investigated the erosive wear of a polyurea coating with a hardness of 95 ShA and a thickness of 3 mm applied to a 3 mm thick plate made of S235 steel. The process of erosive wear was carried out using a stream of compressed air containing abrasive grains of aluminum oxide (Al₂O₃). The erosive wear was studied using different incidence angles (45°, 60° and 90°) and erosive grain sizes. Thus, the effects of the incidence angle and erosive grain size on the erosive wear of the polyurea coating were analyzed. Erosive wear was determined as linear wear: the depth of the wear trace was measured using an optical profilometer. This study showed a non-linear correlation between erosive wear, incidence angle and erosive particle size. In addition, a qualitative study of the surface of the coating after a wear test was carried out using a scanning electron microscope, which made it possible to describe the mechanisms of erosive wear of the polyurea coating. Full article

Aiming at the engineering problems of the severe wear and limited service life of mandrels during the hole extrusion strengthening of critical aerospace components, this study proposes a surface modification strategy for mandrels based on the anti-friction mechanism of micro-textures. Based on the Lame stress equation and the Mises yield criterion, a plastic strengthening stress distribution model of the hole wall was developed. Integrating Bowden’s adhesive friction theory, a parameterized numerical model was constructed to investigate the influence of micro-texture morphology on interfacial friction and wear behavior. An elastic–plastic contact model for micro-textured mandrels during hole extrusion strengthening was established using ANSYS. The effects of key parameters such as the micro-texture depth and area ratio on the contact pressure field, friction stress distribution, and strengthening performance were quantitatively analyzed. The results show that a circular micro-texture with a depth of 50 μm and an area ratio of 20% can reduce the fluctuation and peak value of the contact pressure by 41.0% and 29.7%, respectively, and decrease the average friction stress by 8.1%. The interfacial wear resistance and the uniformity of the residual compressive stress distribution on the hole wall are significantly enhanced, providing tribological insight and surface optimization guidance for improving the anti-wear performance and extending the service life of mandrels. Full article

It has been known for some time that grading of the elastic modulus (namely, softer in the surface) leads to a significant reduction in tensile stresses due to contact loadings; this has been studied mostly to suppress the cracking of brittle materials. In particular, a recent study has demonstrated that the effect is most pronounced for a large Poisson’s ratio, as is the case for incompressible materials. Grading of the modulus occurs intrinsically in viscoelastic materials like rubber when there is a temperature gradient within the rubber, which leads to significant changes of tensile stresses, affecting fatigue and wear. Friction and wear have been analyzed experimentally in the past with respect to mean temperature, revealing an ideal range of temperature with the highest friction and lowest wear, but the effect of the temperature gradient is not as well understood. The present paper presents a simple model of a sinusoidal wave of pressure and shear traction moving on a viscoelastic half-plane (standard material) at constant velocity, finding an approximate solution for a linear variation of viscosity across the depth. We find that tensile stresses may be very significantly altered by temperature changes of a few degrees only across the depth equal to the wavelength of the loading wave. In particular, they are reduced if the temperature decreases with depth, with beneficial effects for fatigue and wear. Full article