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Planetary roller screw mechanism (PRSM) is a novel high-performance linear transmission device. The non-uniformity of load distribution over PRSM threads is a vital problem severely affecting its load-carrying capacity and service performance. This work aims to develop a PRSM load distribution improvement approach with good manufacturability and general applicability. A roller taper modification method for load distribution optimization is proposed based on the deformation compatibility and force equilibrium, then the roller grinding experiment is carried out. Finally, the effects of external load and thread number on load sharing optimization are analyzed to examine the applicability. The results show that the maximum reduction in load sharing coefficient reaches 31.4% after roller taper modification under the case studied. The taper modification of roller threads could be rapidly implemented by only adjusting the roller grinding angle. The roller grinding experiment results agree well with the theoretical values, and the maximum error is no more than 2 μm. As the external load and thread number change, the improvement approach still works well. The proposed method has been verified by comparisons with finite element simulations and available references. This work can provide theoretical guidance for the optimal design of PRSM and has good engineering application prospects.

Correlation between machining errors and transmission accuracy of planetary roller screw mechanism (PRSM) plays an important role in tolerance design. In this study, analytical calculations, machine learning, and experimental verification are used to explore the internal correlation between the machining errors and the transmission accuracy of the PRSM. A multi-roller meshing transmission error model is established, which comprehensively considers the eccentric error, nominal diameter error, flank angle error, and cumulative pitch error of the screw, roller, and nut. The importance coefficients of various machining errors on the transmission error are determined using the random forest algorithm. A genetic algorithm-back propagation neural network algorithm-based method is used to train the dataset generated via analytical calculations. The results show that the proposed analytical calculation model reflects the alternate meshing characteristics of rollers during the PRSM motion, providing a more accurate prediction of the transmission error than the existing prediction methods. For an actual mean travel deviation, the most significant machining error is the cumulative pitch error of the screw, whereas for the actual bandwidth of useful travel, the most significant machining errors are the eccentric errors of the screw and nut. The proposed prediction formulae for transmission error considering the essential machining errors illustrate reasonable prediction accuracy, with an average error of 10.63% for the actual mean travel deviation and 14.27% for the actual bandwidth of useful travel compared with the experiments, which can effectively support the direct design of PRSM tolerance in engineering practice.

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