4.7 Article

Investigation of the decompression condition of double-nut ball screws considering the influence of the geometry error and additional elastic unit

Journal

MECHANISM AND MACHINE THEORY
Volume 156, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.mechmachtheory.2020.104164

Keywords

Ball screws; Load distribution; Decompressing condition; Critical axial load

Funding

  1. National Natural Science Foundation of China [51905274]
  2. National Science and Technology Major Projects of China [2018ZX04039001 and2012ZX04002021]

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This study proposed a modified load distribution model to obtain the critical axial loads of double-nut ball screws and constructed a new test rig to validate the model's accuracy. Analysis showed that the influence of geometry errors on the decompression condition of double-nut ball screws is neglectable compared with the preload.
The critical axial load in the decompression condition is one of the most critical parameters of double-nut ball screws, which was traditionally given at 2.83 times the initial preload in existing studies. However, the traditional idea is mainly on the basis of the assumption that the load acted on the ball within one nut is equal to each other, ignoring the influence of the deformation of the screw shaft and nut body, the geometry error, and the additional elastic units. Therefore, this study proposed a modified load distribution model, based on which both the critical axial loads of double-nut ball screws with and without additional elastic units can be obtained. Simultaneously, a new test rig was constructed for detecting the decompressing condition of double-nut ball screws under different preloading levels, which validates the model built in this study. Further analysis showed that the influence of geometry errors on the decompression condition of double-nut ball screws is neglectable compared with the preload. More importantly, the traditional schematic diagram of the decompression condition of double-nut ball screws was modified. (C) 2020 Elsevier Ltd. All rights reserved.

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