Year-Round Thermal Error Modeling and Compensation for the Spindle of Machine Tools Based on Ambient Temperature Intervals

Abstract

The modeling and compensation method is a common method for reducing the influence of thermal error on the accuracy of machine tools. The prediction accuracy and robustness of the thermal error model are two key performance measures for evaluating the compensation effect. However, it is difficult to maintain the prediction accuracy and robustness at the desired level when the ambient temperature exhibits strong seasonal variations. Therefore, a year-round thermal error modeling and compensation method for the spindle of machine tools based on ambient temperature intervals (ATIs) is proposed in this paper. First, the ATIs applicable to the thermal error prediction models (TEPMs) under different ambient temperatures are investigated, where the C-Means clustering algorithm is utilized to determine ATIs. Furthermore, the prediction effect of different numbers of ATIs is analyzed to obtain the optimal number of ATIs. Then, the TEPMs corresponding to different ATIs in the annual ambient temperature range are established. Finally, the established TEPMs of ATIs are used to predict the experimental data of the entire year, and the prediction accuracy and robustness of the proposed ATI model are analyzed and compared with those of the low and high ambient temperature models. The prediction accuracies of the ATI model are 20.6% and 41.7% higher than those of the low and high ambient temperature models, respectively, and the robustness is improved by 48.8% and 62.0%, respectively. This indicates that the proposed ATI method can achieve high prediction accuracy and robustness regardless of the seasonal temperature variations throughout the year.

A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds

“…This correlation coefficient method selects the temperature variables that are highly correlated with the thermal error as TSPs. However, this method may lose important environmental temperature information, which has significant effects on thermal errors [ 4 ] because the environmental temperature variable is rarely selected as a TSP.…”

“…TSP selection can simplify the model structure or mitigate the collinearity between temperature variables. The idea of TSP selection is to first classify temperature variables into different clusters and then select the most important one from each cluster [ 4 ]. This can effectively prevent the temperature variables from being strongly correlated, and the number of modeling temperature variables (MTVs) is reduced at the same time.…”

A Novel Methodology for Measuring Ambient Thermal Effects on Machine Tools

“…This approach is suitable for small-sized MTs but lacks insight into the "relevant working volume", rendering it insufficient for medium and large MTs. In line with this standard measurement approach, Wei et al [10,11] recently introduced a notable research study that models and compensates for thermal errors in machine tool spindles, particularly in the presence of strong seasonal variations in ambient temperature, introducing the concept of Ambient Temperature Intervals (ATIs). Their work involved conducting year-round experiments to develop a robust model and compare it with other state-of-the-art algorithms, including deep-learning methods, thus demonstrating the effectiveness of their proposal.…”

Thermal Property Analysis of Precision Machinery and Its Thermal Error Compensation

“…In order to avoid and solve the thermal error of precision machining, measures could be taken from the aspect of stabilizing the heat balance of the process system to improve the machining precision [11][12][13][14][15][16][17][18][19][20]. Therefore, figuring out the generation mechanism of the thermal error of precision machinery and building thermal error model for it are conductive to formulating effective thermal error compensation schemes and reducing thermal error, thereby ensuring the machining precision of the products.…”