Abstract
Thermal extension error of boring bar in z-axis is one of the key factors that have a bad influence on the machining accuracy of boring machine, so how to exactly establish the relationship between the thermal extension length and temperature and predict the changing rule of thermal error are the premise of thermal extension error compensation. In this paper, a prediction method of thermal extension length of boring bar in boring machine is proposed based on principal component analysis (PCA) and least squares support vector machine (LS-SVM) model. In order to avoid the multiple correlation and coupling among the great amount temperature input variables, firstly, PCA is introduced to extract the principal components of temperature data samples. Then, LS-SVM is used to predict the changing tendency of the thermally induced thermal extension error of boring bar. Finally, experiments are conducted on a boring machine, the application results show that Boring bar axial thermal elongation error residual value dropped below 5 μm and minimum residual error is only 0.5 μm. This method not only effectively improve the efficiency of the temperature data acquisition and analysis, and improve the modeling accuracy and robustness.
Highlights
In recent years, with the continuous development of equipment manufacturing industry, higher and higher requirement of precision is presented to machine tools [1]
Thermal error of the boring bar is one of the key error sources [2,3], and research shows that the thermal error of boring machine can accounts for 40% ~ 70% of total errors[4,5].In general, the methods to reduce the thermal errors can be classified into error prevent method and error compensation method [6]
Luo et al offered a method based on the stepwise linear regression, which provides a new way for thermal error modeling [13]
Summary
With the continuous development of equipment manufacturing industry, higher and higher requirement of precision is presented to machine tools [1]. Correlation coefficient analysis and fuzzy C means clustering method to reduce the number of temperature sensors, the number of sensors from 32 to 5 [17] This method has the following disadvantages: in order to select the appropriate temperature measurement point must collect a large number of temperature data, the available data must be typical distribution, such as normal (Gaussian) distribution. It can be seen that in the process of the thermal error modeling and compensation, one of the key is to reduce the large number of temperature measuring points. PCA method is proposed to reduce temperature measuring point in this paper, and LS-SVM modeling method is used to predict the thermal error of boring machine. In order to optimize temperature measuring points and realize accurate prediction of thermal extension error, this paper is arranged as following.
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