One-dimensional model for axial thermal error in a micro high-speed spindle

Author(s):  
Van-The Than ◽  
Jin H Huang ◽  
Thi-Thao Ngo ◽  
Chi-Chang Wang

This article proposes a robust and accurate axial thermal error model for a micro high-speed spindle. With measured temperatures, an inverse method is applied to obtain the heat source and temperature field in the spindle for demonstrating that there exists a uniform temperature distribution along the axial direction within the motor range. Hence, a simple one-dimensional heat transfer model is established to acquire the temperature and resulting thermal errors using only one measured temperature on housing surface. Jumped displacement when the spindle starts and stops and the nonlinear deformation on the bearings are satisfactorily treated in the model. The results show that the estimated thermal errors agree with the measured data for both constant and various speeds. In addition, the results reveal that spindle speed significantly affects the maximum thermal error. A short processing time is an advantage of the proposed method. The model promises effective integration in machine tools for compensating thermal errors.

2009 ◽  
Vol 626-627 ◽  
pp. 411-416 ◽  
Author(s):  
Z.C. Wang ◽  
X.L. Hu ◽  
C.H. Zhang

A simplified one-dimensional model, accounting for thermal errors related to high speed spindle of 5-axis CNC machine tools, is developed, and the relationship between heat sources of rotating spindle and thermal deformation in axial direction is found with the help of Fourier’s law for heat transfer under two different boundary conditions. Based on the theory of homogeneous coordinate transformation in robotic, the transformation matrixes between the coordinate system of kinematic pairs and the relationship between errors and compensations are obtained, through which the compensation of thermal errors in high speed motorized spindle is obtainable.


2010 ◽  
Vol 129-131 ◽  
pp. 556-560 ◽  
Author(s):  
Chun Li Lei ◽  
Zhi Yuan Rui

In a lot of factors, thermal deformation of motorized high-speed spindle is a key factor affecting the manufacturing accuracy of machine tool. In order to reduce the thermal errors, the reasons and influence factors are analyzed. A thermal error model, that considers the effect of thermodynamics and speed on the thermal deformation, is proposed by using genetic algorithm-based radial basis function neural network. The improved neural network has been trained and tested, then a thermal error compensation system based on this model is established to compensate thermal deformation. The experiment results show that there is a 79% decrease in motorized spindle errors and this model has high accuracy.


Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 286
Author(s):  
Zhaolong Li ◽  
Bo Zhu ◽  
Ye Dai ◽  
Wenming Zhu ◽  
Qinghai Wang ◽  
...  

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.


2021 ◽  
Author(s):  
Xiangsheng Gao ◽  
Yueyang Guo ◽  
Dzonu Ambrose Hanson ◽  
Zhihao Liu ◽  
Min Wang ◽  
...  

Abstract Thermal error of ball screws seriously affects the machining precision of CNC machine tools especially in high speed and precision machining. Compensation technology is one of the most effective methods to address the thermal issue, and the effect of compensation depends on the accuracy and robustness of the thermal error model. Traditional modeling approaches have major challenges in time-series thermal error prediction. In this paper, a novel thermal error model based on Long Short-Term Memory (LSTM) neural network and Particle Swarm Optimization (PSO) algorithm is proposed. A data-driven model based on LSTM neural network is established according to the time-series collected data. The hyperparameters of LSTM neural network are optimized by PSO and then a PSO-LSTM model is established to precisely predict the thermal error of ball screws. In order to verify the effectiveness and robustness of the proposed model, two thermal characteristic experiments based on step and random speed are conducted on a self-designed test bench. The results show that the PSO-LSTM model has higher accuracy compared with the RBF model and BP model with high robustness. The proposed method can be implemented to predict the thermal error of ball screws, and provide a foundation for thermal error compensation.


2013 ◽  
Vol 303-306 ◽  
pp. 1782-1785
Author(s):  
Chong Zhi Mao ◽  
Qian Jian Guo

The purpose of this research is to improve the machining accuracy of a CNC machine tool through thermal error modeling and compensation. In this paper, a thermal error model based on back propagation networks (BPN) is presented, and the compensation is fulfilled. The results show that the BPN model improves the prediction accuracy of thermal errors on the CNC machine tool, and the thermal drift has been reduced from 15 to 5 after compensation.


2011 ◽  
Vol 189-193 ◽  
pp. 4145-4148
Author(s):  
Qian Jian Guo ◽  
Lei He ◽  
Guang Ming Zhu

Thermal errors are the major contributor to the dimensional errors of a workpiece in precision machining. Error compensation technique is a cost-effective way to reduce thermal errors. Accurate modeling of errors is a prerequisite of error compensation. In this paper, a thermal error model was proposed by using projection pursuit regression (PPR). The PPR method improves the prediction accuracy of thermal deformation in the CNC turning center.


2012 ◽  
Vol 466-467 ◽  
pp. 961-965 ◽  
Author(s):  
Chun Li Lei ◽  
Zhi Yuan Rui ◽  
Jun Liu ◽  
Li Na Ren

To improve the manufacturing accuracy of NC machine tool, the thermal error model based on multivariate autoregressive method for a motorized high speed spindle is developed. The proposed model takes into account influences of the previous temperature rise and thermal deformation (input variables) on the thermal error (output variables). The linear trends of observed series are eliminated by the first difference. The order of multivariate autoregressive (MVAR) model is selected by using Akaike information criterion. The coefficients of the MVAR model are determined by the least square method. The established MVAR model is then used to forecast the thermal error and the experimental results have shown the validity and robustness of this model.


2020 ◽  
Vol 44 (3) ◽  
pp. 344-351
Author(s):  
Cheng Ming Kang ◽  
Chun Yu Zhao ◽  
Jun Qian Zhang

Thermal errors caused by spindle rotation is a major factor that influences the precision stability of CNC machine tools. To determine an effective method for reducing thermal errors, a thermal experiment was carried out on the spindle of a vertical drilling center. The thermal deformation mechanism and thermal error variations of the spindle are presented. Based on the generation, convection, and conduction theory of heat, the thermal field model of a spindle system is derived. The relationship between the thermal field and the radial thermal error is established using a physically based method. Finally, the effect of the thermal error model proposed is verified by both a simulation and experiment. The results recorded on the two CNC machining centers indicate that the average fitting accuracy of the theoretical model is up to 94.1%, which validates the high accuracy and strong robustness of the presented model.


2010 ◽  
Vol 97-101 ◽  
pp. 3211-3214 ◽  
Author(s):  
Xiu Shan Wang ◽  
Yan Li ◽  
Yong Chang Yu

Thermal errors generally account for as much as 70% of the total errors of CNC machine tools, are the most contributor to the workpiece dimensional precision in precision machining process. Thermal error compensation is an effective way to decreasing thermal errors. Precision mode is a key to thermal error compensation. In this paper thermal error modeling method based on the artificial neural networks (ANN) algorithm is applied for a vertical machining center. Four key temperature points of a vertical machining center were obtained based on the temperature field analysis. A novel genetic algorism-Back propagation neural network (GA-BPN) thermal error model was proposed on the basis of four temperature points. The emulations and experiments prove that there was about a 60% increase in machine tool precision.


2010 ◽  
Vol 431-432 ◽  
pp. 110-113
Author(s):  
Xiao Ni Qi ◽  
Qian Jian Guo

The thermal distortion of YK3610 hobbing machine is analyzed. The concept of clustering analysis is proposed and implemented on the gear hobbing machine. The model was used in the experimental of thermal error compensation. The results show that the thermal error compensation control system can reduce thermal errors significantly and the prediction accuracy of the thermal error model is high enough.


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