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Application of Autoregressive Distributed Lag Model to Thermal Error Compensation of Machine Tools

机译:自回归分布滞后模型在机床热误差补偿中的应用

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Since Thermal error in precision CNC machine tools cannot be ignored, it is essential to construct a simple and effective thermal error compensation mathematical model. In this paper, three modeling methods are introduced in detail. The first is multiple linear regression model; the second is congruence model, which combines multiple linear regression model with AR model of its residual error; and the third is autoregressive distributed lag model(ADL), which is compared and analyzed. Multiple linear regression analysis is used most commonly in thermal error compensation, since it is a simple and quick modeling method. But thermal error is nonlinear and interactive, so it is difficult to model a precise least squares model of thermal error. The congruence model and autoregressive distributed lag model belong to time series analysis method which has the advantage of establishing a precise mathematical model. The distinctions between the two models are that: the congruence model divides the parameter into two parts to estimate them respectively, but autoregressive distributed lag model estimates parameter uniformly, so congruence model is less accurate than autoregressive distributed lag model in modeling. This paper, based upon an actual example, concludes that autoregressive distributed lag model for thermal error of precision CNC machine tools is a good way to improve modeling accuracy.
机译:由于精密数控机床的热误差不容忽视,因此构建一个简单有效的热误差补偿数学模型至关重要。在本文中,详细介绍了三种建模方法。第一个是多元线性回归模型;第二个是多元线性回归模型。第二种是同余模型,将多元线性回归模型与残差误差的AR模型相结合。第三是自回归分布滞后模型(ADL),进行了比较和分析。由于多元线性回归分析是一种简单而快速的建模方法,因此最常用于热误差补偿中。但是热误差是非线性的并且是相互作用的,因此很难对热误差的精确最小二乘模型进行建模。一致性模型和自回归分布滞后模型属于时间序列分析方法,具有建立精确数学模型的优点。两种模型之间的区别在于:同余模型将参数分为两部分分别进行估计,而自回归分布滞后模型对参数进行统一估计,因此在建模中,同余模型的准确性不及自回归分布滞后模型。本文以一个实际例子为基础得出结论,针对精密数控机床热误差的自回归分布滞后模型是提高建模精度的一种好方法。

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