基于周期分解的机床主轴回转误差预报模型

The Predictive Model Based on Periodic Decomposition of Spindle Rotation Induced Error for Machine

随着精密机械、惯性技术和宇宙科学等学科的发展 ,超高精密零件在这些领域有着重要用途。但是其制造以及测量均受到主轴回转误差的限制。当今加工预测补偿控制已成为精密加工技术的一个发展研究方向。依靠传统的主轴回转误差预测模型仅仅能补偿掉误差成分中可表达的重复误差。本文对主轴回转误差采用了基于周期分解的组合建模方法 ,建立了更有效的预测模型。仿真实验结果表明该模型提高了回转误差的预报精度 。

The manufacturing and measurements of the ultra precision hardware have been more and more important with the development of the science of precision machinery. However it is limited by the spindle rotation induced error. It is imprecise to compensate the expressible deterministic part of errors using the traditional method. A new compounding modeling method based on the periodic decomposition is proposed and discussed . Further more a more effective model has been built. The simulation experiments show that the predictive model has improved the forecasting precision of spindle rotation induced error and establish the application base for the forecasting compensatory control.