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The raceway profile greatly influences the performance of a ball screw. Although the traditional circular raceway profile is simple in structure, it can be a limitation to the load carrying capacity of ball screws. In this paper, a novel type of ball screw raceway profile is proposed based on geometrical close contact theory. Firstly, the second-order approximation curve of the ball is derived using the McLaughlin formula in the normal plane of the raceway helix, which serves as the basis for constructing the close raceway profile. The mathematical expression of the contact stress distribution of the close raceway is then obtained using Chebyshev polynomials. A comparison and analysis of the contact stress distribution between the close raceway and the conventional raceway is carried out. Furthermore, simulation models of ball screws with the close raceway profile are established and the contact behavior of the ball with both types of raceways is statically simulated using ANSYS software. The dynamic performance of both ball screws is compared and analyzed using ADAMS software. The analysis results show that compared with the conventional raceway ball screw, the close raceway ball screw has lower stress, strain, and friction torque under the same load conditions. These research results have significant implications for the structural optimization of ball screws. [ABSTRACT FROM AUTHOR]

Recently, bearing fault diagnosis methods based on deep learning have achieved significant success. However, in practical engineering applications, the limited labeled data and various working conditions severely constrain the widespread application of most deep-learning-based fault diagnosis methods. Additionally, many methods focus solely on the amplitude information of samples, neglecting the rich relational information between samples. To address these issues, this paper proposes a novel cross-condition few-shot fault diagnosis method based on an adaptive dynamic threshold graph neural network (ADTGNN). The aim of the proposed method is to rapidly identify fault types after they occur only a few times or even once. The adaptive threshold computation module (ATCM) in ADTGNN dynamically assigns thresholds to each edge based on edge confidence, optimizing the graph structure and effectively alleviating the over-smoothing issue. Furthermore, a dynamic threshold adjustment strategy (DTAS) is introduced to gradually increase the threshold with the training iterations, preventing the model from prematurely discarding crucial edges due to insufficient performance. The proposed model's effectiveness is demonstrated using three bearing datasets. The experimental results indicate that the proposed approach significantly outperforms other comparison methods in cross-condition bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

The lubrication effects of two types of universal lubrication methods for ball screw mechanism are firstly discussed.For lubrication in oil,the variation situation of the thinnest lubricating oil film thickness vary with the ball screw rotation is figured out according to the Hamrock-Dowson expression and the fact that the ball screw always runs on conditions of boundary lubrication is identified by the calculation of the film thickness ratio.For lubrication in grease,the fluctuation curve of the ball speed responds to the ball screw rotation is figured out through the analysis of their mutual motion and geometric relationship.The curve demonstrates that using the grease to lubricate the ball screw mechanism with high rotation rate will cause higher temperature rise.To overcome the above two shortages,a dynamic grease coating lubrication method is proposed.Lastly,a friction torque stabilization test and a temperature rise test are conducted to prove the superiority of the proposed method.