Abstract
For the purpose to improve a design quality of high-speed spindle units, we have developed mathematical models and software to simulate a rotation accuracy of spindles running on ball bearings. In order to better understand the mechanics of ball bearings, the dynamic interaction of ball bearings and spindle unit, and the influence of the bearing imperfections on the spindle rotation accuracy, we have carried out computer aided analysis and experimental studies. When doing this, we have found that the spindle rotation accuracy can vary drastically with rotational speed. The influence of bearing preload has a secondary importance. Comparison of the results of these studies has demonstrated adequacy of the models developed to the real spindle units.
Highlights
One of the most important features of a spindle unit (SU) is its accuracy of spindle rotation
In order to better understand the mechanics of ball bearings, the dynamic interaction of ball bearings and spindle unit, and the influence of the bearing imperfections on the spindle rotation accuracy, we have carried out computer aided analysis and experimental studies
We have found that the spindle rotation accuracy can vary drastically with rotational speed
Summary
One of the most important features of a spindle unit (SU) is its accuracy of spindle rotation. We define the imperfection of spindle rotation (the inverse to the spindle rotation accuracy) as a root-mean-square sum of the harmonics (amplitudes) in the spectral decomposition of spindle vibration, excluding the harmonic at the speed of rotation. We term this sum the spindle run-out. Averaged summation of the spectrum amplitudes gives us a general measure of vibrodisplacements, which, being doubled, we term the run-out Such a spectrum depends on disturbances (produced by non-ideal bearings in our case), elastic, and damping properties of the SU structure. Following the purposes stated above, we have developed the complex mathematical model of high-speed SUs [1], one of the elements of which is the dynamic model (Figure 1)
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