Thermal error modeling and compensation based on Gaussian process regression for CNC machine tools

Wei, Xinyuan; Ye, Honghan; Miao, Enming; Qing, Pan

doi:10.1016/j.precisioneng.2022.05.008

Cited by 49 publications

(12 citation statements)

References 34 publications

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“…At the same time, an LSTM layer and a fully connected layer are designed. For more details on the structure of the LSTM and its training options, please refer to [ 10 ].…”

Section: Existing Thermal Error Modeling Algorithmsmentioning

confidence: 99%

“…The GPR algorithm can be used for thermal error modeling [ 10 ], which has good prediction effects. The GPR algorithm is selected as the third baseline method for prediction comparison in this study.…”

Section: Existing Thermal Error Modeling Algorithmsmentioning

confidence: 99%

“…In the second step of a mathematical thermal error modeling method, establishing a thermal error prediction model is the key to achieving satisfactory prediction effects. Various algorithms have been applied to thermal error modeling, such as backpropagation neural network (NN) [ 5 ], support vector machine [ 6 ], multiple linear regression [ 7 , 8 ], state-space [ 9 ], and Gaussian process regression (GPR) [ 10 ]. Notably, the NN [ 5 ] only consists of a single hidden layer whereas the deep learning method is more complicated with multiple hidden layers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Wei

Zhou

et al. 2023

Sensors

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Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack interpretability. Therefore, this paper proposes a regularized regression algorithm for thermal error modeling, which has a simple structure that can be easily implemented in practice and has good interpretability. In addition, automatic temperature-sensitive variable selection is realized. Specifically, the least absolute regression method combined with two regularization techniques is used to establish the thermal error prediction model. The prediction effects are compared with state-of-the-art algorithms, including deep-learning-based algorithms. Comparison of the results shows that the proposed method has the best prediction accuracy and robustness. Finally, compensation experiments with the established model are conducted and prove the effectiveness of the proposed modeling method.

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“…At the same time, an LSTM layer and a fully connected layer are designed. For more details on the structure of the LSTM and its training options, please refer to [ 10 ].…”

Section: Existing Thermal Error Modeling Algorithmsmentioning

confidence: 99%

Section: Existing Thermal Error Modeling Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Wei

Zhou

et al. 2023

Sensors

Self Cite

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show abstract

“…The thermal error modeling theory generally includes TEPM establishment and TSP selection algorithms. Principal component regression (PCR) [ 26 ], ridge regression [ 20 ], and partial least squares [ 27 , 28 ] are the common partial regression algorithms that can effectively suppress the influence of collinearity between input variables. Based on our preliminary studies, we used the PCR algorithm to construct the TEPM and the correlation coefficient algorithm to select the TSPs, which are introduced in Section 2.1 and Section 2.2 , respectively.…”

Section: Thermal Error Modeling Algorithmsmentioning

confidence: 99%

“…Recently, researchers have further studied the thermal error modeling algorithm of CNC machine tools [ 18 , 19 , 20 , 21 , 22 ] to improve the accuracy and robustness of thermal error prediction. However, the thermal error data in these studies had very small variations of ambient temperature, and the influence of ambient temperature was rarely considered.…”

Section: Introductionmentioning

confidence: 99%

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

Wei

Feng

2022

Sensors

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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.

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Multi-objective process parameter optimization considering minimum thermal accumulation on spindles of dry hobbing machine

Yang

et al. 2023

Int J Adv Manuf Technol

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Thermal error modeling and compensation based on Gaussian process regression for CNC machine tools

Cited by 49 publications

References 34 publications

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

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

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

Multi-objective process parameter optimization considering minimum thermal accumulation on spindles of dry hobbing machine

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