一种用于机床主轴的缺陷驱动诊断方法。
A defect-driven diagnostic method for machine tool spindles.
作者信息
Vogl Gregory W, Donmez M Alkan
机构信息
Engineering Laboratory, National Institute of Standards and Technology (NIST), 100 Bureau Drive, Gaithersburg, Maryland 20899-8220, USA.
出版信息
CIRP Ann Manuf Technol. 2015;64(1):377-380. doi: 10.1016/j.cirp.2015.04.103. Epub 2015 May 5.
Simple vibration-based metrics are, in many cases, insufficient to diagnose machine tool spindle condition. These metrics couple defect-based motion with spindle dynamics; diagnostics should be defect-driven. A new method and spindle condition estimation device (SCED) were developed to acquire data and to separate system dynamics from defect geometry. Based on this method, a spindle condition metric relying only on defect geometry is proposed. Application of the SCED on various milling and turning spindles shows that the new approach is robust for diagnosing the machine tool spindle condition.
在许多情况下，基于简单振动的指标不足以诊断机床主轴状态。这些指标将基于缺陷的运动与主轴动力学结合在一起；诊断应该以缺陷为驱动。开发了一种新方法和主轴状态估计装置（SCED）来获取数据，并将系统动力学与缺陷几何形状分离。基于此方法，提出了一种仅依赖于缺陷几何形状的主轴状态指标。将SCED应用于各种铣削和车削主轴表明，这种新方法在诊断机床主轴状态方面具有鲁棒性。
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