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The application feedback on existing silicon nitride ceramic bearings and RCF experimental research all indicate that the primary failure mode of silicon nitride ceramic bearings is material spalling on the contact surface. Spalling failure occurs due to the initiation and propagation of cracks under rolling contact. However, silicon nitride ceramic bearings, owing to their unique manufacturing method, inevitably exhibit defects and cracks. Therefore, as silicon nitride ceramic bearings are increasingly prevalent, reducing the probability of spalling failure is crucial for extending their service life. This can only be achieved by gaining a clear understanding of the crack initiation and expansion mechanisms in silicon nitride ceramic bearings. This paper is based on silicon nitride rolling friction experiments. It involves the joint simulation of Franc3D-V8.4 and ABAQUS2020, wherein the crack front SIFs are calculated for each load contact position of the surface crack on the silicon nitride ceramic bearing ring during cyclic movement. The study also delves into the determination of the maximum effective stress intensity factors and explores the influence of the initial crack depth on the cycle life and direction of crack propagation. The research yields several valuable conclusions. The findings of this research offer theoretical guidance for formulating grinding technologies for silicon nitride rings and adjusting and controlling working parameters of silicon nitride ceramic ball bearings. These insights are crucial for enhancing the reliability and longevity of silicon nitride ceramic bearings in practical applications.

This paper solves the leader-following consensus problem for a class of second-order multi-agent systems with input quantized by a newly proposed adaptive dynamic quantizer. The novel dynamic quantizer is an adaptive quantizer that combines the logarithmic quantizer and the uniform quantizer by introducing dynamic gain parameters to achieve quantizer adaptive adjustment. It has advantages of logarithmic, uniform, and adaptive dynamic quantizers in ensuring reducible communication expenses and acceptable quantizer errors for better system performance. On this basis, we transform the guide way climbing frame (GWCF) under ideal conditions into a second-order multi-agent system and solve the motion synchronization problem of GWCF. Finally, we illustrate our approach by numerical examples.

For full ceramic ball bearings, cracks occur frequently in the spalling on the rings, which leads to impacts on the bearing dynamic characteristics. In this paper, the spalling is set on the outer ring, and the dynamic model considering the effect of crack is proposed. The crack is considered to be related to the strain energy, and the effect on the stiffness of the outer ring is also analyzed. Results show that the appearance of cracks leads to the reduction of the full ceramic bearing stiffness, and the vibration amplitude of bearing increases. The effect of a crack depends on its size, and the vibration of the bearing with cracks of different widths and depths vary greatly. This study provides theoretical basis for the calculation of full ceramic bearing and is of great significance for the state monitoring and fault diagnosis.

The thermal characteristics of a motorized spindle are the main determinants of its performance, and influence the machining accuracy of computer numerical control machine tools. It is important to accurately predict the thermal field of a motorized spindle during its operation to improve its thermal characteristics. This paper proposes a model to predict the temperature field of a high-speed and high-precision motorized spindle under different working conditions using a finite element model and test data. The finite element model considers the influence of the parameters of the cooling system and the lubrication system, and that of environmental conditions on the coefficient of heat transfer based on test data for the surface temperature of the motorized spindle. A genetic algorithm is used to optimize the coefficient of heat transfer of the spindle, and its temperature field is predicted using a three-dimensional model that employs this optimal coefficient. A prediction model of the 170MD30 temperature field of the motorized spindle is created and simulation data for the temperature field are compared with the test data. The results show that when the speed of the spindle is 10,000 rpm, the relative mean prediction error is 1.5%, and when its speed is 15,000 rpm, the prediction error is 3.6%. Therefore, the proposed prediction model can predict the temperature field of the motorized spindle with high accuracy.