Precision manufacturing heavily relies on high-speed motorized spindle equipment. Correspondingly, thermal errors generated by spindles severely curb achievable machining accuracy and stand out as a critical restrictive element. Existing thermal error models often rely on redundant sensor configurations and suffer from degraded prediction performance under varying speed conditions. This study presents a two-stage approach for motorized spindle thermal error prediction. First, we construct a thermomechanical coupled finite element model for characterizing temperature distribution and guiding the layout of measuring sensors. The Temporal Correlation–Variance Inflation Factor (TC-VIF) method is then utilized for optimization of temperature measuring positions, cutting the number of sensors from seven to four and removing multicollinearity. For thermal error modeling, a hybrid framework is developed in which second-order polynomial regression with ridge regularization captures the fundamental temperature–deformation mapping, while a two-layer LSTM models the residuals to compensate for dynamic nonlinearity. For cross-condition validation, the model is trained on three speed conditions and tested on two unseen conditions. Results show that the proposed model achieves the lowest MAE on the test set, demonstrating superior average prediction accuracy. Compared to PSO-SVM, SSA-KELM, WOA-BP, and TCN benchmarks, the hybrid model reduces MAE and RMSE by 44.8% and 40.0% on average, respectively, and achieves a coefficient of determination R² of 0.95 on the training conditions. This enhanced cross-condition generalization is attributed to the effective integration of physics-informed modeling with data-driven learning.

During the outer contour grinding of silicon nitride (Si3N4), the material’s hard and brittle properties have been shown to significantly accelerate the wear of diamond grinding wheels. This necessitates frequent wheel dressing during machining, thus rendering grinding wheel wear a pivotal factor in determining the contour accuracy and surface finish of the roller’s external cylindrical surface. In conventional practice, dressing strategies are predominantly empirical, and the underlying mechanism by which wheel wear induces profile errors remains insufficiently elucidated. In order to address these challenges, a profile error prediction model was developed based on grinding ratio theory, with the radial wear of the grinding wheel being a specific consideration. The investigation systematically explored the influences of roller geometry, wheel specifications, and grinding parameters on the evolution of profile errors, with the cumulative number of processed rollers designated as the independent variable. Taking a ϕ 5 mm × 5.4 mm silicon nitride spherical roller with a profile error threshold of 5 μm as an example, it is necessary to ensure that the grinding wheel is dressed after no more than 20 rollers have been machined consecutively. The maximum deviations between the model predictions for grinding wheel wear (∆R) and profile error (∆ε) and the experimental values were 17.605

The effects of roller skew on the wear in tapered roller bearing are investigated in the current work. A quasi-static model considering roller skew is established, and a wear depth calculation model suitable for tapered roller bearings is derived. The wear depth of raceways and roller elements in tapered roller bearings with and without roller skew are compared. The effects of bearing speed, load, friction coefficient, and cage pocket clearance on wear depth are analyzed. The results revealed that the bearing load and friction coefficient significantly affect the roller skew angle and the bearing wear distribution. A 25

Under complex friction conditions, base oils usually exhibit insufficient friction-reducing and anti-wear performance, poor shear resistance of the lubricating film, and weak interfacial adsorption stability. Herein, graphene oxide (GO) was surface-modified with the silane coupling agent KH550 and compounded with Ag nanoparticles to fabricate a silver/modified graphene oxide (Ag/KGO) composite lubricant additive. The microstructure and chemical characteristics of the Ag/KGO composite were characterized by SEM, XRD, FTIR, and Raman spectroscopy. Tribological tests performed on a Si3N4/GCr15 friction pair demonstrated that the lubricant containing 0.15 wt% Ag/KGO achieved the optimal tribological performance, with the average friction coefficient decreasing to 0.053, 51.8% lower than that of the base oil, and the wear scar width and depth decreasing by 34.5% and 75.7%, respectively. Molecular dynamics simulations revealed that Ag/KGO enhanced the interfacial adsorption strength and improved the shear stability of the lubricating film. Mechanism analysis indicated that KGO facilitated the formation of a stable lubricating film at the friction interface, while Ag nanoparticles acted as nano-bearings. Their synergistic effect reduced interfacial shear resistance and alleviated wear. These findings provide theoretical support for the design and development of high-performance composite lubricant additives.

This paper investigates the grinding mechanism and optimization of process parameters for spherical surfaces in silicon nitride joint bearings to enhance their grinding precision and efficiency. First, the effects of grinding wheel speed, workpiece speed, feed depth, and abrasive size on surface roughness, material removal rate, and equivalent grinding depth are analyzed based on the abrasive grain trajectory and material removal mechanism. Subsequently, a quadratic function model for the spherical surface grinding evaluation parameters is established using the response surface methodology. Finally, an improved multi-objective particle swarm optimization model is established, incorporating a process constrained discrete sampling strategy for grinding parameters to achieve precise and efficient grinding of spherical surfaces under multi-pass conditions. Experimental results demonstrate that the developed response surface model exhibits high predictive accuracy, with errors for surface roughness, material removal rate, and equivalent grinding depth below 0.0176 mu m, 0.3615 mm3/min, and 3.8 mu m, respectively. The grinding efficiency for the spherical surfaces was increased by 21.22% following the parameters optimization. The results provide a theoretical basis and a practical strategy for the actual grinding process.

The accurate prediction of bearing wear is critical for ensuring the reliability, lifespan, and performance of mechanical systems. However, the existing research on bearing wear prediction often suffers from limited accuracy and fails to account for the multi-factorial and nonlinear nature of the wear process. As bearings are subjected to complex conditions, such as varying loads, friction frequencies, and environmental temperatures, improving the precision of wear predictions under these conditions is essential for better performance evaluation and maintenance planning. This study addresses these limitations by exploring the multi-factorial and nonlinear relationships in bearing wear prediction and enhancing the predictive accuracy of existing models. To investigate the wear behavior of various bearing materials, this study employs the RTEC multifunctional friction-wear testing machine, which performs reciprocating sliding wear tests under dry friction conditions. Three different bearing pairs, Si3N4-Si3N4, Si3N4-GCr15, and GCr15-GCr15, were tested under various operational parameters, including wear duration, load, friction frequency, and ambient temperature. These conditions simulate real-world operational environments and provide a comprehensive dateset of friction and wear data, which is foundational for the predictive model. A novel prediction model based on a back propagation (BP) neural network optimized by an improved sparrow search algorithm (CSSA-BP) is proposed. This model incorporates input factors such as the bearing material type, wear duration, load, friction frequency, and ambient temperature to predict the wear amount of bearings under different conditions. The CSSA-BP model can improve the precision and robustness of wear predictions by accounting for the complex interactions between these multiple factors. The CSSA-BP model performance is evaluated using key statistical metrics. The determination coefficient (R-2) was 0.980 1, indicating that the model explained 98.01% of the variance in the wear data. The mean squared error (MSE) was 0.046 7 and the mean absolute error (MAE) was 0.162 7, both of which suggested high accuracy and low error rates in the model predictions. These results demonstrate that the CSSA-BP model not only achieves a high degree of accuracy but also offers excellent fitting performance for predicting bearing wear under various operating conditions, provides valuable insights into the wear mechanisms of different bearing pairs, and offers an innovative approach to predicting wear using an optimized neural network model. The CSSA-BP model effectively enhanced the prediction accuracy and stability of wear estimates, which is crucial for improving the maintenance strategies and operational efficiency of bearings in industrial applications. By integrating multiple operational factors, the model surpasses the limitations of previous methods that rely on fewer input variables or simpler linear relationships. This study combines the sparrow search algorithm with a BP neural network, which allows the model to effectively handle the nonlinear and complex interactions among various input variables. The results indicate that the CSSA-BP model is a promising tool for predicting bearing wear, offering significant improvements in both prediction accuracy and computational efficiency compared to traditional models. This study provides a comprehensive and reliable method for predicting bearing wear across different operating conditions. The proposed CSSA-BP model contributes to a better understanding of bearing wear behavior and also holds significant potential for real-world applications in the predictive maintenance and performance optimization of bearings, ultimately extending the service life and reliability of mechanical systems.

To address the challenges of directly accessing the internal temperature field of electric spindle, the heavy computational burden and limited of real-time applicability associated with conventional physics based models, and the restricted cross condition robustness of purely data driven framework under small sample sizes and varying operating conditions, this study develops a cross condition temperature prediction framework for electric spindle that transfers knowledge from finite element simulation to real-world measurements, combining proper orthogonal decomposition with Physics-Augmented GRU (PA-GRU) and domain adversarial alignment. First, transient temperature field data under multiple operating conditions are obtained via finite element simulations, and the high dimensional temperature field is decomposed into spatial modes and time coefficients to obtain a low dimensional representation. Second, physical constraints based on thermal equilibrium mechanisms are imported into the GRU to enhance the framework’s physical consistency. Finally, a data acquisition system is developed to gather actual data across various operating conditions. By combining this with domain-specific adversarial learning mechanisms, the discrepancy between simulation derived and measurement derived features is reduced, thereby enhancing transferability across operating regimes. Experimental results show that under low, medium, and high test conditions, the proposed method maintains both MAE and RMSE within 1 °C, with an accuracy rate exceeding 90

Hot Isostatic Pressing Silicon Nitride (HIPSN) full-ceramic ball bearings have excellent properties including light weight, wear resistance, and good accuracy retention, which are promising for application in high-end precision machine tool spindle systems. The service performance of spindle bearings directly determines the operational performance of precision machine tools. In order to improve its performance, it is necessary to optimize the design of full-ceramic angular contact ball bearings. In this study, the internal macrostructural parameters of the bearings are considered as design parameters, with the raceway wear rate and stiffness serving as the objectives. Based on the Advanced Dynamics of Rolling Elements (ADORE) software, response surface models are established using Response Surface Method (RSM) to characterize the correlation between design parameters and objectives. Optimization is performed using multi-objective genetic algorithm (MOGA) to obtain multiple sets of optimized solutions. Compared to the original bearing design parameters, the maximum reduction in raceway wear rate of 9

Full-ceramic ball bearings possess superior properties, including low density, resistance to both high and low temperatures, corrosion resistance, and electrical insulation, which give them broad application prospects under extreme working conditions. However, a ceramic inner ring fatigue fracture failure was observed during the actual operating tests. Such failure mode and its underlying mechanism are not reported and remain insufficiently understood, which restricts reliable prediction of full-ceramic bearing service life. In current study, the fatigue fracture behavior of the ceramic inner ring was systematically characterized, and a fatigue fracture mechanism governed by the superimposed tensile stress field induced by the interference fit with the steel shaft and the Hertzian contact at the ball–raceway interface was proposed. Specifically, cracks initiate at subsurface volumetric defects within the stress concentration region beneath the raceway groove bottom. Under the superimposed tensile stress field, the main fracture crack initiates and propagates radially toward the interior of the inner ring during the early stage of crack growth. As the crack extends inward, the contribution of the tensile stress induced by Hertzian contact gradually decreases, whereas the hoop tensile stress caused by the interference fit increases and becomes dominant. When the crack length reaches a critical value, rapid unstable propagation occurs, ultimately resulting in catastrophic fracture of the ceramic inner ring. These findings provide crucial insight into the fatigue fracture mechanism of ceramic bearing inner rings and offer practical guidance for the structural design and engineering application of full-ceramic rolling bearings.

Silicon nitride ceramics have important application value in aerospace, high temperature bearing and other fields because of its excellent high temperature strength, oxidation resistance and thermal shock resistance. However, the intrinsic brittleness of Si3N4 ceramics limits its reliability under extreme conditions. The fracture toughness of Si3N4 ceramics is usually 3-6 MPa m1/2, which is far from meeting the needs of extreme working conditions such as aerospace engines. The low fracture toughness of Si3N4 is mainly attributed to its strong covalent bond in crystal structure and microstructure defects. Hence, the structural characteristics and intrinsic brittleness sources of Silicon nitride ceramics are reviewed, and the second phase toughening mechanism and microstructure design strategy are emphatically analyzed. By introducing metal particles (e.g., Fe, Mo), hard particles (e.g., TIC, SiC), whisker/fiber (e.g., SiCw) and phase change particles (ZrO2), the fracture toughness of Si3N4 ceramics was significantly improved by combining the cooperative mechanisms of crack deflection, bridging and phase change induction. In addition, the toughening effect of microstructural design strategies such as grain orientation optimization, hierarchical layered structure design, the control of (3-Si3N4 grain aspect ratio, interfacial thermal stress distribution, and crack extension paths are also discussed. This paper will provide a theoretical reference for the preparation of high toughness Si3N4 ceramics and promote its engineering application in extreme environments.

To prevent the failure of silicon nitride hip joint materials caused by friction and wear, microcrystalline diamond coatings, nanocrystalline diamond coatings, and graded micro-nano composite diamond coatings were deposited on silicon nitride substrates by hot filament chemical vapor deposition (HFCVD). Scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray diffraction (XRD) and Raman spectroscopy (Raman) were employed to characterize the nucleation, film quality, surface and cross-sectional morphologies, and surface roughness of the prepared diamond coatings. The adhesion strength between the coatings and the substrates was analyzed using a Rockwell hardness tester. The biotribological performance of the diamond coatings was evaluated using a ball-on-disk reciprocating tribometer with fetal bovine serum (FBS) as the lubricant. The results showed that the graded micro-nano composite diamond coating, possessing a three-layer structure consisting of microcrystalline, sub-microcrystalline and nanocrystalline diamond, significantly improved the biotribological performance of the silicon nitride hip joint. It exhibited the lowest wear rate (1.68 × 10⁻⁸mm³/(N·m)), good adhesion strength with the substrates (HF3), and a smooth surface, effectively preventing the delamination and squeaking phenomena observed in single-layer coatings. Therefore, it is an ideal surface modification solution for artificial hip joints.

Aiming at the fact that it is difficult to exert the best performance of the spindle under different working conditions by adjusting the preload of the motorized spindle bearing, to realize the self-adjustment of the preload of the bearing, an intelligent adjusting component of the preload of the spindle with the advantages of strong adaptability, fast response, and high resolution is proposed. Based on the inverse piezoelectric effect of piezoelectric ceramics, the pre-tightening force regulating component of the spindle bearing is designed, and a finite element analysis model is established to explore the dynamic stiffness response of the spindle in the speed range. The thermodynamic model of the spindle is established, and the dynamic and thermal characteristics of controllable preload spindle under different working conditions are explored. The results show that the static measurement error of the control module is less than 2 N, which meets the requirements of spindle preload control. Within the range of the sweep frequency setting, the maximum displacement of the shaft end is 0.08 mm, and the dynamic stiffness of the spindle does not fluctuate significantly, which meets the design requirements. Compared with constant pressure preload, the active control method of spindle bearing preload based on the control module can reduce spindle vibration by about 13

Conventional spherical surface generating grinding of silicon nitride spherical plain bearings is challenged by a low material removal efficiency and rapid grinding wheel wear. This paper proposes a non-uniform contact grinding method for silicon nitride spherical plain bearings based on conventional spherical surface generating grinding method. Introducing an axial eccentricity between the rotate axis of the grinding wheel and the spherical center along axial direction of the bearing ring increases cooling and lubrication effect of the grinding zone, thereby improving material removal efficiency and the grinding wheel wear resistance. Initially, the spherical surface generation mechanism and the calculation method for the grinding wheel positioning coordinates in the non-uniform contact mode are analyzed. Subsequently, a method for identifying the grinding wheel wear profile parameters and a calculation method for grinding ratio is proposed, based on the principle of convergence between the spherical radius and the grinding wheel wear profile radius. Finally, a prediction model for the positioning coordinates of the grinding wheel after eccentric movement is developed by using the radius of the outer edge of the wear profile. A prediction model for the spherical radius is also established based on a cubic polynomial fitting model of the grinding ratio. Experimental results illustrate that the non-uniform contact grinding method with an eccentricity of 0.1 mm improves the average grinding ratio by 52.8

A novel method, energy method, for solving bearing stiffness in rotor system supported by multiple bearings is proposed. Energy method has no restrictions on bearing type, bearing arrangement, bearing number and load type in rotor bearing system and can significantly simplify the solution procedures involved in determining bearing displacements and bearing stiffness. The potential energy model of flexible rotor bearing system is derived by combining finite element method and bearing load–displacement relationship and can be expressed as the function of rotor shaft nodes’ displacements. Based on the principle of minimum potential energy, the true displacements of all nodes in the system are calculated by optimization algorithm, and then the stiffness for each bearing is obtained. The effectiveness of the proposed energy method is verified by comparing with the results of bearing displacements, loads and stiffness coefficients in published literatures. Based on the proposed energy method, the effects of the rotor shaft flexibility, bearing arrangement, load position, bearing radial clearance and initial angular misalignment of outer ring caused by installation error on bearing stiffness in the rotor bearing system are investigated.

In response to the insufficient research on the relationship between the surperfinishing process of raceway in silicon nitride full ceramic ball bearings and temperature rise in highend applications, this paper investigates the effects of surperfinishing process parameters on raceway surface roughness and the mechanism by which roughness affects temperature rise, using a combination of experimental and theoretical analysis methods. The experiments revealed that the influence of surperfinishing time, pressure, tangential speed, long-stroke oscillation frequency, and short-stroke vibration frequency on surface roughness decreases in that order. Based on the experimental results, an optimal superfinishing parameter combination was determined, which achieved a 90.8

To explore how surface textures affect the adhesion between the diamond film and YG8 cemented carbide, this study prepared micro-nano-diamond coatings via HFCVD on substrates with six textures: wavy, hexagonal, concentric circular, inscribed circular, grid, and zigzag. Raman, XRD, SEM, and AFM were used to characterize nucleation, morphology, crystalline quality, and roughness; the friction coefficient and wear rate were tested under dry sliding. The wavy texture performed best: at a methane concentration of 5

On-machine measurement is a critical technology that enhances manufacturing precision and efficiency in the production of spherical surfaces for joint bearings. However, the accuracy of fitting reference ball center coordinates for short arc measurements utilizing a lever gauge remains low. The simple calculation of the distance between reference ball center and the calibration points does not suffice for precise identification of pre-travel error. This limitation significantly compromises the measurement accuracy of spherical surfaces. Therefore, this paper proposes a novel method for establishing and identifying a pre-travel error prediction model specifically for on-machine measurements conducted with a lever gauge. Initially, the mechanical structure of the lever gauge and the principles governing on-machine measurement of spherical surface was analyzed. This analysis focused on mechanism of pre-travel error, considering factors such as motion, contact force, and probe pose. Subsequently, a strategy for selecting calibration points was developed, tailored to the measurement requirements spherical surfaces in joint bearings. The parameters of the pre-travel error prediction model were determined using measurement data from reference ball calibration points, which were collected by the lever gauge at various pre-travel distances. The efficacy of the pre-travel error compensation method was ultimately verified through on-machine measurements of spherical surfaces on reference balls and plain bearings. The results indicate that, pre-travel error compensation significantly reduces the measurement error for spherical surfaces on reference balls to less than 0.7 mu m, thereby improving the measurement accuracy by 57.1 %. For spherical surfaces in joint bearings, the measurement error after compensation is decreased to less than 1.4 mu m, resulting in an improvement in measurement accuracy of 53.3 %. The compensation results show that the proposed prediction model for pre-travel error can improve the on-machine measurement accuracy considerably.

Purpose-This study aims to clarify the effect of three-dimensional surface topography of the raceway on the friction characteristics of Hot Isostatic Pressing Silicon Nitride (HIPSN) full ceramic ball bearings under fully dry friction conditions, so as to improve the friction and wear performance and reduce the frictional heat generation of the bearings. Design/methodology/approach-This study uses three-dimensional surface roughness parameters to quantitatively characterize the surface topography of the bearing raceway after superfinishing. This paper conducts a test study on HIPSN full ceramic angular contact ball bearings under fully dry friction conditions using a bearing friction and wear test rig and using scanning electron microscope to observe the raceway surfaces of the outer rings. Findings-The three-dimensional surface topography of the raceway has a significant effect on the frictional characteristics of HIPSN full ceramic ball bearings in dry friction. The correlation degree ranking is S-a>S-q>S-sk>S-ku>S-dr>S-dq. Within the range of test parameters, the bearing friction coefficient is proportional to the rotational speed and radial load. Reducing S-a, S-q, S-sk and S-ku to achieve a uniform height distribution of asperities and reduce the sharpness of rough peaks can effectively reduce the friction coefficient and improve the bearing's frictional performance. Originality/value-This study provides a theoretical basis for the evaluation of frictional performance of the ceramic bearing raceway surface and has great significance for improving the operational characteristics and service life of Si3N4 full ceramic ball bearings under fully dry friction conditions. Peer review-The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2025-0190/

Silicon nitride is widely used in ceramic bearings. Silicon nitride-based Ti-DLC films with different low doped Ti content were prepared by multifunctional high frequency magnetron sputtering system, to expand the application of silicon nitride in aerospace and modern industry by improving its tribological properties. SEM, Raman, XPS and AFM were used to analyze the structure and composition of the films. The mechanical properties were measured by nanoindentation and scratch test. The tribological performances were studied at room temperature and the friction and wear mechanism was analyzed. Ti-DLC films with low doped Ti content present a dense amorphous structure. The content of sp3-C in Ti-DLC films increases first and then decreases, with the increase of doped Ti content. It means that the increase of doped Ti content improves the hardness, elastic modulus and the adhesion behaviors. Moreover, the increase of doped Ti content reduces friction coefficient and wear rate, which is beneficial to tribological performance. The tribological performances of silicon nitride-based Ti-DLC films are optimal when the doped Ti content is 0.64 at. %, and the average friction coefficient is as low as 0.006 which is close to super slippery and the wear rate is 3.68 x 10-7 mm3 center dot N-1 center dot m-1. The design prepared the ultra-low friction Ti-DLC film on the silicon nitride can reduce harmful friction losses, creating application possibilities of silicon nitride in harsh conditions. Particularly, it provides new ideas and technical support for the manufacture of selflubricating silicon nitride bearings.