球速度变化时分离器力的估计模型

A. Leveille, P. Frantz, Garry Rosene
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引用次数: 1

摘要

当球轴承上的组合载荷包括径向或力矩方向的力时,球将不会以一个共同的速度绕轴承运行。如果球的速度变化足够大,使球从其正常间距扩展到超过保持架袋间隙的量,则这种球速度变化(BSV)可能会导致额外的轴承摩擦力。在本文中,我们提出了一个模型,用于估计笼力和摩擦力矩引起的BSV,并将模型预测与实测摩擦力矩测试数据进行比较。该模型首先通过确定球在轴承中心的单一轨道上必须滑动的距离来分析单个球上的力。球对球圈的作用力是由滑移过程中损失的能量与内圈界面处的牵引力输入的能量相等决定的。球到笼的力的分量法向球-袋界面,然后通过平衡这个力与球到赛跑力来确定。笼子到地面的力量同样是由平衡所有球到比赛和球到笼子界面的集体力量决定的。最后,BSV阻力扭矩写成这三个阻力源贡献的总和。为了验证该模型,使用在施加推力和径向载荷下运行的单个204尺寸轴承获得了试验数据。发现轴承拖动扭矩取决于不对准和保持架口袋间隙的程度,正如模型预测的那样。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Model to Estimate Separator Forces during Ball Speed Variations
When the combined loads on ball bearings include forces in the radial or moment directions, the balls will not orbit the bearing at one common speed. This ball speed variation (BSV) might result in additional bearing friction forces if the variation in speed is large enough to allow the balls to spread out from their normal spacing by an amount that exceeds the cage pocket clearances. In this paper we present a model used to estimate the cage forces and friction torques caused by BSV and compare the model predictions with measured friction torque test data. The model first analyzes the forces on a single ball by determining the distance over which a ball must slip during a single orbit of the bearing center. The ball to race force is determined by equating the energy lost during this slip with the energy input through traction at the inner race interface. The component of the ball to cage force normal to the ball–pocket interface is then determined by balancing this force with the ball to race force. The cage to land force is similarly determined by balancing the collective forces at all the ball to race and ball to cage interfaces. Finally, the BSV drag torque is written as the sum of contributions from these three drag sources. In order to validate the model, test data were obtained using single 204 size bearings operating under applied thrust and radial loading. The bearing drag torque was found to depend on the degree of misalignment and cage pocket clearance, as predicted by the model.
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