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Harmonic gears always work under different operating conditions and may usually break down due to lubrication failures, while its lubrication mechanism is still not clearly understood. This paper aims to present a lubrication model comprehensively considering the influence of contact geometry, lubrication properties and three-dimensional (3D) real surface roughness to analyze the lubrication performance under different conditions.

Based on the discrete convolution-fast Fourier transformation with duplicated padding and quasi-system numerical methods, the lubrication model for harmonic gears is developed, which is verified by comparing results with available lubrication data.

The effects of meshing process, working conditions and 3D roughness on the lubrication characteristics are discussed. From the calculated cases, the increase in rotational speed and decrease of applied torque may increase the film thickness, enhancing the lubrication performance of harmonic gears. It is also observed that proper surface roughness can be used for lubrication design.

The research results can provide theoretical guidance for improving lubrication performance and reducing friction/wear of the harmonic gear interfaces. This study can be promoted to various engineering scenarios of harmonic gears, such as industrial robots, space-driven agencies and precision measuring instruments.

This study aims to use a machine learning (ML) model for the prediction of traction coefficient and asperity load ratio for different surface topographies of non-conformal rough contacts.

The input data set for the ML model is generated using a mixed-lubrication model. Surface topography parameters (skewness, kurtosis and pattern ratio), rolling speed and hardness are used as input features in the multi-layer perceptron (MLP) model. The hyperparameter tuning and fivefold cross-validation are also performed to minimize the overfitting.

From the results, it is shown that the MLP model shows excellent accuracy (R² > 90%) on the test data set for making the prediction of mixed lubrication parameters. It is also observed that engineered rough surfaces with high negative skewness, low kurtosis and isotropic surface patterns exhibit a significant low traction coefficient. It is also concluded that the MLP model gives better accuracy in comparison to the random forest regression model based on the training and testing data sets.

Mixed lubrication parameters are predicted by developing a regression-based MLP model. The machine learning model is trained using several topography parameters, which are vital in the mixed-EHL regime because of the lack of regression-fit expressions in previous works. The accuracy of MLP with random forest models is also compared.

Hydro-viscous drive (HVD) clutches are widely used in equipment requiring soft start, such as fans and pumps, to transmit torque and adjust speed by changing the gap distance between friction pairs. This paper aims to propose a novel two-parameter evaluation method for HVD during the mixed lubrication stage. The objective is to develop an effective model that establishes the relationship between these parameters and the actual surface topography.

In the presented methods, the fractal features of the real manufacturing surface are calculated based on the power spectrum function by the ultra-depth three-dimensional microscope. After that, the hybrid friction model of the friction plate is established based on mixed elasto-hydrodynamic lubrication theory, boundary friction model and fractal theory. Then the torque and load bearing characteristics of the clutch are obtained, and the influences of the surface fractal features are investigated and discussed. Finally, the Weierstrass–Mandelbrot function is adopted for the surface topography characterization and evaluation.

The results indicate that the proposed method exhibits good accuracy, while the speed difference between the friction pair exceeds 2,500 rpm. It is concluded that this paper proposed a way to evaluate the torque and loading capacity of HVD considering the real manufacturing surface topography and is helpful for surface optimization.

The originality and value of this study lie in its development of a novel torque and load bearing capacity evaluation method for HVD in mixed lubrication stage, considering manufacturing surface topography and describing the real manufacturing surface.

This study aims to investigate the effects of irregular groove textures on the friction and wear performance of sliding contact surfaces. These textures possess multiple depths and asymmetrical features. To optimize the irregular groove texture structure of the sliding contact surface, an adaptive genetic algorithm was used for research and optimization purposes.

Using adaptive genetic algorithm as an optimization tool, numerical simulations were conducted on surface textures by establishing a dimensionless form of the Reynolds equation and setting appropriate boundary conditions. An adaptive genetic algorithm program in MATLAB was established. Genetic iterative methods were used to calculate the optimal texture structure. Genetic individuals were selected through fitness comparison. The depth of the groove texture is gradually adjusted through genetic crossover, mutation, and mutation operations. The optimal groove structure was ultimately obtained by comparing the bearing capacity and pressure of different generations of micro-convex bodies.

After about 100 generations of iteration, the distribution of grooved textures became relatively stable, and after about 320 generations, the depth and distribution of groove textures reached their optimal structure. At this stage, irregular texture structures can support more loads by forming oil films. Compared with regular textures, the friction coefficient of irregular textures decreased by nearly 47.01%, while the carrying capacity of lubricating oil films increased by 54.57%. The research results show that irregular texture structures have better lubrication characteristics and can effectively improve the friction performance of component surfaces.

Surface textures can enhance the friction and lubrication performance of metal surfaces, improving the mechanical performance and lifespan of components. However, surface texture processing is challenging, as it often requires multiple experimental comparisons to determine the optimal texture structure, resulting in high trial-and-error costs. By using an adaptive genetic algorithm as an optimization tool, the optimal surface groove structure can be obtained through simulation and modeling, effectively saving costs in the process.

This paper aims to improve the tribological properties of lithium complex greases using nanoparticles to investigate the tribological behavior of single additives (nano-TiO₂ or nano-CeO₂) and composite additives (nano-TiO₂–CeO₂) in lithium complex greases and to analyze the mechanism of their influence using a variety of characterization tools.

The morphology and microstructure of the nanoparticles were characterized by scanning electron microscopy and an X-ray diffractometer. The tribological properties of different nanoparticles, as well as compounded nanoparticles as greases, were evaluated. Average friction coefficients and wear diameters were analyzed. Scanning electron microscopy and three-dimensional topography were used to analyze the surface topography of worn steel balls. The elements present on the worn steel balls’ surface were analyzed using energy-dispersive spectroscopy and X-ray photoelectron spectroscopy.

The results showed that the coefficient of friction (COF) of grease with all three nanoparticles added was low. The grease-containing composite nanoparticles exhibited a lower COF and superior anti-wear properties. The sample displayed its optimal tribological performance when the ratio of TiO₂ to CeO₂ was 6:4, resulting in a 30.5% reduction in the COF and a 29.2% decrease in wear spot diameter compared to the original grease. Additionally, the roughness of the worn spot surface and the maximum depth of the wear mark were significantly reduced.

The main innovation of this study is the first mixing of nano-TiO₂ and nano-CeO₂ with different sizes and properties as compound lithium grease additives to significantly enhance the anti-wear and friction reduction properties of this grease. The results of friction experiments with a single additive are used as a basis to explore the synergistic lubrication mechanism of the compounded nanoparticles. This innovative approach provides a new reference and direction for future research and development of grease additives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2023-0291/

As the crucial support component of the propeller power system, the reliability of the operation of submersible pumps is influenced by the lubrication performance of water-lubricated thrust bearings. When the water-lubricated thrust bearings are under start-stop or heavy load conditions, the effect of surface morphology is crucial as the mixed lubrication regime is encountered. This paper aims to develop one mixed lubrication model for the water-lubricated thrust bearings to predict the effects of surface skewness, kurtosis and roughness orientation on the loading carrying capacity and tribological behavior.

This paper developed one improved mixed lubrication model specifically for the water-lubricated thrust bearing system. In this model, the hydrodynamic model was improved by using the height of the rough surface and its probability density function, combined with the average flow model. The asperity contact model was improved by using the equation for the Pearson system of frequency curves to characterize the non-Gaussian aspect of surface roughness distribution.

According to the results, negative skewness, large kurtosis and lateral surface pattern can improve the tribological performance of water-lubricated thrust bearings. Optimizing the surface morphology is a reasonable design method that can improve the performance of water-lubricated thrust bearings.

In this paper, one mixed lubrication model specifically for the water-lubricated thrust bearing with the effect of surface roughness into consideration was developed. Based on the developed model, the effect of surface morphology on tribological behavior can be evaluated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0247/

Tribological research is complex and multidisciplinary, with many parameters to consider. As traditional experimentation is time-consuming and expensive due to the complexity of tribological systems, researchers tend to use quantitative and qualitative analysis to monitor critical parameters and material characterization to explain observed dependencies. In this regard, numerical modeling and simulation offers a cost-effective alternative to physical experimentation but must be validated with limited testing. This paper aims to highlight advances in numerical modeling as they relate to the field of tribology.

This study performed an in-depth literature review for the field of modeling and simulation as it relates to tribology. The authors initially looked at the application of foundational studies (e.g. Stribeck) to understand the gaps in the current knowledge set. The authors then evaluated a number of modern developments related to contact mechanics, surface roughness, tribofilm formation and fluid-film layers. In particular, it looked at key fields driving tribology models including nanoparticle research and prosthetics. The study then sought out to understand the future trends in this research field.

The field of tribology, numerical modeling has shown to be a powerful tool, which is both time- and cost-effective when compared to standard bench testing. The characterization of tribological systems of interest fundamentally stems from the lubrication regimes designated in the Stribeck curve. The prediction of tribofilm formation, film thickness variation, fluid properties, asperity contact and surface deformation as well as the continuously changing interactions between such parameters is an essential challenge for proper modeling.

This paper highlights the major numerical modeling achievements in various disciplines and discusses their efficacy, assumptions and limitations in tribology research.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2023-0076/

Due to high speeds, heavy loads, large slide-to-roll ratios (SRR) and other variable operating conditions, some rolling bearings that have been working in harsh conditions may experience flash temperatures in the contact area, which may result in early damage like smearing and then affect service life. This study aims to investigate the flash temperature phenomenon of rolling bearings through theoretical and experimental analysis.

A technology for measuring temperature distribution in rolling ball on disk contact under lubrication was developed. The test-rig can simulate the ball bearing contact. The effects of working conditions such as entrainment speed, load, SRR and lubricating oil viscosity on the flash temperature were investigated.

The results of the theoretical calculation and experiments indicate that the parameters promoting the reduction of film thickness in elastohydrodynamic lubrication are always related with the number of flash points, even film thickness reduced to mixed lubrication. The flash temperature is easier to happen in conditions of high SRR, heavy load, slow entrainment speed and low viscosity oil.

This work conducts an experimental study on the flash temperature phenomenon, providing a test technology for bearing lubrication and failure investigation.

This author has opted into Transparent Peer Review available at: https://publons.com/publon/10.1108/ILT-04-2023-0104

This paper aims to study the impact of transient velocity changes on sealing performance during reciprocating sealing processes.

Establish a model of transient mixed lubrication, solve the transient Reynolds equation, consider the effect of temperature rise at the seal interfaces, and determine the behavior of the seal interfaces, such as film thickness and fluid pressure. Evaluation with friction and leakage rate, calculate the variation of sealing performance with reciprocating velocity under different working conditions, and verify it through bench experiments.

Within a reciprocating stroke, the frictional force decreases with increasing velocity, and the frictional force of the outstroke is greater than that of the instroke; at the time of the stroke transition, the fluid pressure is smallest and the rough peak contact pressure is greatest. At present, the dynamic pressure effect of fluids is the largest, and the friction force also increases, which increases the risk of material wear and failure. Friction and leakage increase with increasing pressure and root mean square roughness. As temperature increases, friction increases and leakage decreases. In studying the performance variations of seal components through a reciprocating sealing experiment, it was found that the friction force decreases with increasing velocity, which is consistent with the calculated results and more similar to the calculated results considering the temperature rise.

This study provides a reference for the study of transient sealing performance.

The purpose of this study is to develop a transient wheel–rail rolling contact model to primarily investigate the rail damage under wet condition when the train passes through the welded joints.

The impact force induced by welded joints is obtained through vehicle–track coupling dynamics. The normal and tangential wheel–rail contact pressures were solved by elastohydrodynamic lubrication (EHL) theory and simplified third-body layer theory, respectively. Then, the obtained tangential pressure and normal pressure were applied to the finite element model as moving loads, simulating cyclic loading. Finally, the shakedown map and critical plane method were used to predict rolling contact fatigue (RCF) and the initiation of fatigue cracks.

The results indicate that RCF will occur and fatigue cracks are more prone to appear on the subsurface of the rail, specifically around 2.7 mm below the rail surface in the vicinity of the welded joint and its heat-affected zone.

The cosimulation of numerical model and finite element model was implemented. The influence of surface roughness and fluids was considered. In this model, the normal and tangential wheel–rail contact pressure, the stress and strain and the rail fatigue cracks were obtained under a rail-welded joint excitation.