Temperature-sensitive point selection and thermal error modeling of spindle based on synthetical temperature information

Li, Zheyu; Li, Guolong; Xu, Kai; Tang, Xiaodong; Dong, Xiaomin

doi:10.1007/s00170-021-06680-9

Cited by 29 publications

(11 citation statements)

References 34 publications

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“…(6) Judge whether the fitness function value has reached the set accuracy or the maximum iteration number. If it is full, then step (7). Otherwise, return to step (5) to continue the iteration.…”

Section: Bas-bp Neural Network Modelmentioning

confidence: 99%

“…This method reduces the number of temperature measurement points, eliminates multicollinearity between temperature variables, and improves the accuracy of the thermal error model. Li et al [7] proposed a temperature-sensitive point selection method based on integrated temperature information (STI), which solved the problem of incomplete clustering and the same number of temperature-sensitive points with different errors. Chiu et al [8] used the Pearson correlation coefficient method to remove the temperature point with low correlation.…”

Section: Introductionmentioning

confidence: 99%

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Research on Thermal Error Modeling of Motorized Spindle Based on BP Neural Network Optimized by Beetle Antennae Search Algorithm

Zhu

Dai

et al. 2021

Machines

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High-speed motorized spindle heating will produce thermal error, which is an important factor affecting the machining accuracy of machine tools. The thermal error model of high-speed motorized spindles can compensate for thermal error and improve machining accuracy effectively. In order to confirm the high precision thermal error model, Beetle antennae search algorithm (BAS) is proposed to optimize the thermal error prediction model of motorized spindle based on BP neural network. Through the thermal characteristic experiment, the A02 motorized spindle is used as the research object to obtain the temperature and axial thermal drift data of the motorized spindle at different speeds. Using fuzzy clustering and grey relational analysis to screen temperature-sensitive points. Beetle antennae search algorithm (BAS) is used to optimize the weights and thresholds of the BP neural network. Finally, the BAS-BP thermal error prediction model is established. Compared with BP and GA-BP models, the results show that BAS-BP has higher prediction accuracy than BP and GA-BP models at different speeds. Therefore, the BAS-BP model is suitable for prediction and compensation of spindle thermal error.

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Section: Bas-bp Neural Network Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Thermal Error Modeling of Motorized Spindle Based on BP Neural Network Optimized by Beetle Antennae Search Algorithm

Zhu

Dai

et al. 2021

Machines

View full text Add to dashboard Cite

show abstract

“…Guo et al [10] determined the best temperature variable by grouping search, eliminating the multicollinearity between temperature variables. Li et al [11] proposed a temperature-sensitive point selection method based on integrated temperature information (STI), which solved the problem of incomplete clustering and the same number of temperature-sensitive points with different errors. Li et al [12] used SOM neural network to cluster the temperature measurement points and used the correlation analysis method to explore the correlation between the thermal sensitive points and the thermal error of the spindle.…”

Section: Introductionmentioning

confidence: 99%

Thermal error modeling of motorized spindle based Elman neural network optimized by Sparrow Search Algorithm

Li¹,

Zhu²,

Dai

et al. 2022

Preprint

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The thermal error of the motorized spindle is an essential factor affecting the machining accuracy of high-speed CNC machines. The establishment of a high-speed motorized spindle thermal error model for thermal error compensation can effectively improve the impact of thermal errors on the machining accuracy of the machine tool. This paper proposes a sparrow search algorithm to optimize the Elman neural network to predict thermal errors in motorized spindles. First, thermal characteristics of the A02 high-speed motorized spindle were analyzed using ANSYS Workbench software. Based on the simulation results, the position of the temperature measuring points is arranged in the temperature and thermal error experiment of the motorized spindle. The temperature and thermal displacement data of high-speed motorized spindle at different rotational speeds were collected; Secondly, the method of combining pedigree clustering and k-means clustering is used to perform cluster analysis on each temperature measurement point, and the grey correlation degree is used to determine the correlation between temperature measurement points and thermal error. Three temperature-sensitive points were screened from ten temperature measurement points, which reduced the collinearity between temperature measurement points and the number of independent variables of the model. Finally, the weights and thresholds of the Elman neural network are optimized by the sparrow search algorithm, and the thermal error prediction model of motorized spindle based on SSA-Elman neural network is established and compared with Elman Neural Network and Particle Swarm optimized Elman Neural Network prediction model. The results show that the SSA-Elman neural network model has the highest prediction accuracy and exhibits good stability and generalization ability.

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“…Thermal error contributes to about 70% of the total machining error in precision machine tools [1][2][3][4]. Spindle is one core component of a precision machine tool, thermal error of the spindle is the major factor leads to the deterioration of machining accuracy for machine tools [5][6][7]. Precision machine tools usually preheats for long period to achieve thermal equilibrium before machining.…”

Section: Introductionmentioning

confidence: 99%

“…Much effort has been made to study high-quality data-driven regression modeling methods for the spindle thermal error, and the modeling methods are widely used in the thermal error compensation for various spindles [6,[13][14][15]. It seems such regression modeling methods can also be used for the thermal error feedback control, the onlinetemperature measurements can be used as the inputs for the spindle thermal error regression model, and the model can output feedback in real-time for the thermal error feedback control.…”

Section: Introductionmentioning

confidence: 99%

Feedback control–based active cooling with pre-estimated reliability for stabilizing the thermal error of a precision mechanical spindle

Lei

Gao

et al. 2022

Int J Adv Manuf Technol

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Thermal error stability (STE) of the spindle determines the machining accuracy of a precision machine tool. Here we propose a thermal error feedback control based active cooling strategy for stabilizing the spindle thermal error in long-term. The strategy employs a cooling system as actuator and a thermal error regression model to output feedback. Structural temperature measurements are considerably interfered by the active cooling, so the regression models trained with experimental data might output inaccurate feedbacks in unseen work conditions. Such inaccurate feedbacks are the major cause for excessive fluctuations and failures of the thermal error control processes. Independence of the thermal data is analyzed, and a V-C (Vapnik-Chervonenkis) dimension based approach is presented to estimate the generalization error bound of the regression models. Then, the model which is most likely to give acceptable performance can be selected, the reliability of the feedbacks can be pre-estimated, and the risk of unsatisfactory control effect will be largely reduced.Experiments under different work conditions are conducted to verify the proposed strategy, the thermal error is stabilized to be within a range smaller than 1.637μm, and thermal equilibrium time is advanced by more than 78.3%.

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Temperature-sensitive point selection and thermal error modeling of spindle based on synthetical temperature information

Cited by 29 publications

References 34 publications

Research on Thermal Error Modeling of Motorized Spindle Based on BP Neural Network Optimized by Beetle Antennae Search Algorithm

Research on Thermal Error Modeling of Motorized Spindle Based on BP Neural Network Optimized by Beetle Antennae Search Algorithm

Thermal error modeling of motorized spindle based Elman neural network optimized by Sparrow Search Algorithm

Feedback control–based active cooling with pre-estimated reliability for stabilizing the thermal error of a precision mechanical spindle

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