Friction最新文献

Overcoats are predominantly employed to tackle tribological challenges in numerous moving mechanical systems. However, when overcoats are thinned down to sub-10 nm levels, their performance gets significantly compromised because of the dominance of surface and interface effects. Here, we discovered the efficacy of the chemistry of sub-10 nm thick carbon-based overcoats in regulating the friction and wear of rough ceramic surfaces, particularly those of Al₂O₃+TiC (AlTiC). Carbon overcoats up to 4 nm in thickness grown with low-energy (~4–5 eV) atoms/ions caused no significant changes in the tribological performance of AlTiC. However, carbon overcoats grown at a moderate energy of 90 eV experienced an exceptional reduction in friction and wear of AlTiC at similar thickness levels up to 4 nm. The addition of a 6 nm thick RF-sputtered carbon layer on top of these carbon overcoats caused no significant improvement in the tribological performance. However, the addition of a multilayer graphene overlayer was found to slightly reduce the friction further for the thicker carbon overcoats grown at 90 eV. Chemical bonding and carbon microstructural analyses, along with ion interaction simulations, were performed to elucidate the fundamental mechanisms behind the observed friction and wear performances. We discovered that the atomic mixing and high sp³ bonding caused by the 90 eV growth process primarily dictated the friction and wear control at ≤ 10 nm overcoat thicknesses. Thus, by adopting suitable carbon overcoat technology, excellent tribological properties can be attained even at sub-5 nm overcoat thickness levels, which is critical for numerous applications.

Assessing wear is an indispensable task across almost all engineering disciplines, and automated wear assessment would be highly desirable. To determine the occurrence of wear, machine learning strategies have already been successfully applied. However, classifying different types of wear remains challenging. Additionally, data scarcity is a major bottle neck that limits the applicability of machine learning models in certain areas such as biomedical engineering. Here, we present a method to accurately classify surface topographies representing the three most common types of mechanically induced wear: abrasive, erosive, and adhesive wear. First, a random forest (RF) classifier is trained on a list of parameters determined from 3-dimensional (3D) surface scans. Then, this method is adapted to a small dataset obtained from damaged cartilage tissue by using knowledge transfer principles. In detail, two random forest models are trained separately: a base model on a large training dataset obtained on synthetic samples, and a complementary model on the scarce cartilage data. After the separate training phases, the decision trees of both models are combined for inference on the scarce cartilage data. This model architecture provides a highly adaptable framework for assessing wear on biological samples and requires only a handful of training data. A similar approach might also be useful in many other areas of materials science where training data are difficult to obtain.

The microscopic topography of friction surfaces and system structural parameters are both critical factors influencing the characteristics of friction-induced vibration (FIV). However, no existing analytical model for FIV has incorporated these factors. To address this issue, we developed a novel coupled model to explore the combined effects of surface microscopic topography and structural parameters on the FIV characteristics. Furthermore, we conducted two friction-induced vibration and noise (FIVN) simulation experiments to validate the conclusions derived from the numerical simulations. The results showed a strong correlation between the microscopic surface morphological parameters and the friction surface's contact properties. A higher fractal dimension increases contact stiffness, whereas a larger fractal scale factor reduces contact stiffness. The contact damping initially increases and then decreases with changes in the fractal dimension. The surface microscopic parameters significantly affect the modal coupling characteristics and FIV. In a certain range of fractal dimension, modal coupling takes place in the friction system, and with an increase in the fractal scale factor, the region of system instability also grows. FIVN simulation experiments showed that smoother friction surfaces tend to result in high-intensity FIVN. Regarding the structural parameters, when the contact interface has a large fractal dimension and scale factor, structural changes do not significantly affect the system's modal coupling. However, when these parameters decrease, structural parameters exert a more substantial influence on modal coupling. In particular, when both the fractal dimension and scale factor are small, a reduced block thickness does not affect system stability, and FIV also minimal. As the thickness increases, modal coupling and unstable vibrations emerge in the system. Thus, for new brake pads with large block thicknesses, such as those used in high-speed trains, increasing the fractal dimension and scale factor of the friction surface is recommended to reduce high-intensity FIVN in the saturation stage.

Self-dispersed graphene crumpled ball (GCB) demonstrates exceptional tribological performance as lubricant additive under elevated temperature. However, the critical relationship between its unique wrinkle architecture, internal porosity characteristic, and the resultant dispersion stability/friction-reduction mechanism remains insufficiently explored. Particularly, the synergistic effects arising from structural hierarchy and surface chemistry modulation in high-temperature lubrication systems require systematic investigation. Herein, we propose a wrinkle engineering strategy guided by Stokes' law to fabricate surface modifier-free GCB with programmable three-dimensional geometries. Systematic investigations reveal that the degree of wrinkling on the GCB critically dominates the dispersion characteristics and the interlayer shearing resistance. Upon the molybdenum disulfide quantum dots deposited on GCB, a more consistent and robust tribo-chemical reaction film can be formed on the friction interface and in response to protect from severe damage. This complex achieves over 2-fold enhancement in antifriction efficiency compared with commercial high-temperature chain oil (CH-27Q). Overall, this study establishes a structure-performance paradigm for developing autonomous lubrication systems under extreme thermal conditions.

The role of additives in liquid superlubricity is regarded as a crucial element of the running-in process due to their role in reducing friction. Nevertheless, there has been minor investigation into rheological changes that occur during the process. This paper presents an examination of the evolution of film thickness over time and its subsequent behavior. The primary experiments were performed on an optical ball-on-disk tribometer, with the ability to control the percentage of slip. The film thickness was evaluated by optical interferometry and its rheological behavior was subsequently researched by rotational rheometer and viscometer. It was discovered that the primary contribution to the reduction in friction during running-in is better contact separation caused by the evaporation of water. However, the global behavior of the solution was found to have been changed by formation of a convoluted compound and probably by adsorption to contact surfaces. It causes a behavior that is more complex than that predicted by common elastohydrodynamic equations, but may result in a reduction of friction due to an increased separating layer.

Graphene and fullerene nanoparticles exhibit remarkable tribological performance in solid-liquid composite lubrication systems. However, the atomic-scale understanding of how surface topography influences their tribological behavior and performance is still limited. Herein, the influence mechanisms of surface topography features (achieved by regulating asperity amplitude and frequency parameters) on system lubrication performance and nanoparticle friction behavior were systematically investigated through friction experiments and molecular simulations. The results indicate that, at the micro-nanoscale, the amplitude parameter predominantly governs the surface roughness features and frictional resistance. This is because an increased amplitude strengthens the boundary lubrication effect, exacerbates stress concentration and structural deformation of graphene, and makes fullerene more likely to fill grooves and difficult to bear normal loads, thereby exacerbating friction and wear (friction coefficient increased by 59%). In contrast, the frequency parameter primarily determines the surface kurtosis features and normal force. At low frequency, low kurtosis features intensify the normal squeezing effect of asperities, inducing the hydrodynamic pressure effect of the base oil, thus enhancing lubrication performance (friction coefficient decreased by 22%). Compared with frequency, the pronounced influence of amplitude on lubrication state and interface contact behavior dominates the tribological properties of the system and the lubrication mechanism of the nanoparticles. Lower surface roughness and kurtosis features are critical for achieving efficient lubrication. This study offers valuable insights into the design of surface topography and the optimization of lubrication performance.

Tungsten disulfide (WS₂) nanomaterials have emerged as highly effective lubricant additives, leveraging their capacity to mitigate friction and wear, enhance operational performance, and prolong the durability of sliding components. This review provides a comprehensive overview of recent advances in the preparation methods of WS₂ nanomaterials and their applications in tribology. It evaluates how preparation techniques, surface modifications, and composite architectures govern their friction-reducing properties, elucidating the mechanistic underpinnings of their superior tribological performance. WS₂ nanomaterials are reported to exhibit superior tribological properties, positioning them as a prominent research frontier in materials science and tribological engineering. Their industrial implementation holds substantial potential for generating both economic benefits and societal value through enhanced energy efficiency and extended component lifespan. Despite the promising potential of WS₂ nanomaterials in next-generation lubrication technologies, significant challenges hinder their widespread practical application. These include understanding how defect dynamics impact lubrication performance, addressing the inherent limitations of non-polar oil matrices, the lack of comprehensive knowledge regarding real-time service behavior under operational conditions, and their restricted applicability in extreme environments. Overcoming these critical barriers is crucial to fully realize the sustainable potential of WS₂ nanomaterials in advanced lubrication solutions.

The dynamic load and transient lubrication effects seriously influence the friction torque of ball screw actuators under high frequency reciprocating conditions. However, the available studies rarely consider the transient effects of rough surfaces under dynamic loads. In this paper, a dynamic friction torque model for ball screw actuators is proposed, integrating low-order finite elements with transient mixed lubrication. Dynamic contact loads are solved based on the tribo-dynamic model accounting for mechanism vibration. The lubrication, friction, and stiffness under time-varying velocities and dynamic loads are systematically analyzed. The accuracy of the model is verified by experimentally measured dynamic friction torque under high frequency reciprocation. Within the unified model, the dynamic friction behavior of ball screw actuators subjected to combined high-frequency reciprocation and complex loads are analyzed. The findings demonstrate that locating bearings exhibit superior lubrication performance compared to ball screws, primarily due to their lower sliding and spinning speeds, which result in significantly reduced friction torque. Amplitude escalation expands both the high load area and sliding/spinning speeds, thereby causing a friction torque increment. The study provides theoretical support for the dynamic performance optimization of ball screw actuators.

This study presents a comprehensive investigation into the synthesis, dispersion behavior, and performance evaluation of surfactant-free copper oxide (CuO) nanoballs (NBs) dispersed in polyalphaolefin (PAO) oil. CuO NBs were synthesized via a modified precipitation technique and characterized using X-ray diffraction (XRD), Raman spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM), confirming their monoclinic crystal structure and spherical morphology with particle sizes ranging from 25 to 132 nm. The dispersion quality and long-term stability of nanolubricants were assessed using UV–Vis spectroscopy and zeta potential analysis, which indicated that 0.01 wt% CuO achieved the highest stability (zeta potential: 154.3 mV) and minimal sedimentation up to 10 days. Rheological measurements showed Newtonian behavior across all concentrations, with the highest relative viscosity observed at 0.05 wt% and 100 °C. The viscosity index improved at lower concentrations, supporting the lubricant’s thermal adaptability under dynamic shear conditions. Thermal conductivity increased with CuO addition, peaking at 0.01 wt%, primarily due to enhanced Brownian motion and reduced nanoparticle agglomeration. Tribological performance, evaluated using a reciprocating tribometer under a 10 N load and 840 m stroke length, revealed that 0.01 wt% CuO achieved a 37% reduction in the coefficient of friction (COF) (0.055) and the lowest specific wear rate among all tested samples. Surface analysis via 3D profilometry and SEM/EDS revealed smoother contact surfaces and no evidence of CuO deposition, suggesting a rolling friction mechanism as the dominant lubrication mode. These findings confirm that surfactant-free CuO NBs significantly enhance the tribological, rheological, and thermal properties of PAO oil, offering a cost-effective and environmentally friendly solution for high-performance industrial lubrication systems.

The insufficient dispersion and random orientation of nanofillers in composite materials fundamentally constrain the enhancement of their tribological properties. To address these inherent limitations, a strategy was developed to assemble graphene oxide (GO) and hexagonal boron nitride (h-BN) into three-dimensional interconnected architectures (3DGB) via directional freeze-casting, achieving controlled alignment of these components. The sheet-sheet integration of h-BN and graphene nanosheets facilitates structural stabilization of 3DGB network through interfacial stress redistribution mechanisms, concurrently improving fracture resistance characteristics. The fabricated 3DGB serves as an optimized framework substrate for epoxy resin (EP) composites in resin transfer molding method, yielding substantial improvements in tribological property while achieving synergistic enhancements in both load-bearing capacity and interfacial adhesion. Comparative analysis demonstrates that the 3DGB/EP composites exhibit a concurrent enhancement in properties of combination relative to pristine epoxy. Specifically, their 37.5% increase in tensile strength and 33% thermal conductivity enhancement compared to pristine epoxy. Notably, 3DGB significantly boosts the tribological performance of epoxy, evidenced by 72.1% reduction in kinetic friction coefficients and 90.12% decrease of specific wear rates. This strategy establishes a novel paradigm for hierarchical design of high-performance composites and offers new insights into the integration of multi-component 2D fillers and tribology-based multifunctional composites.