Thermal sensor selection for the thermal error modeling of machine tool based on the fuzzy clustering method

Wang, Haitong; Wang, Liping; Li, Tiemin; Han, Jian

doi:10.1007/s00170-013-4998-6

Cited by 46 publications

(16 citation statements)

References 17 publications

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“…The common advantages of the existing mathematic statistics method include the following: a large number * Chengxin Zhang qfzcx_sd@163.com Feng Gao gf2713@126.com of sensors can be arranged on a machine tool, and several optimal measurement points can then be selected from the arranged sensors through a statistical analysis [4][5][6][7][8][9][10][11][12]. However, there are certain disadvantages of this type of method:…”

Section: Mathematical Statistics Methodsmentioning

confidence: 99%

A novel linear virtual temperature constructing method for thermal error modeling of machine tools

Zhang

Gao

Zhenhua

et al. 2015

Int J Adv Manuf Technol

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When a thermal error model of a machine tool is established, selecting the appropriate temperature measurement point is a very difficult problem. This paper proposes a novel method for constructing a linear virtual temperature. The proposed method can overcome the problem of selecting the temperature measurement point. First, temperaturethermal expansion hysteresis characteristics are used to divide the temperature measurement points into two groups. Each temperature variable is then chosen through a principal component analysis (PCA). Finally, using two temperaturevariable weights and the correlative coefficient thermal displacement as the maximum optimal indexes, two temperatureweighted coefficients are calculated, and a linear virtualtemperature variable related to the thermal error linearity is then formed. In establishing the proposed thermal error model, the linear virtual temperature formed can serve as a system input variable. The proposed method was tested on a threeaxis milling machine to determine the spindle Z-axial thermal error, and the results show that the root mean square error (RMSE) is reduced by 11 % and the sum of the squares of the error (SSE) is reduced by 39 % in comparison with a direct application of a temperature variable when establishing such a model. In the proposed method, only two temperature measurement points are used to establish a model, through which the complexity in determining the optimal measurement points through a traditional method, along with the number of temperature measurement points required, is greatly reduced.

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Section: Mathematical Statistics Methodsmentioning

confidence: 99%

A novel linear virtual temperature constructing method for thermal error modeling of machine tools

Zhang

Gao

Zhenhua

et al. 2015

Int J Adv Manuf Technol

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“…The axial load is acquired by the pressure sensor installed with the relief valves. According to the experiences of selecting heat-sensitive points [17,18], temperature sensors are mainly set at the nearest places to heat sources. In the experiments, temperature sensors are mainly placed at drive motor housings, left/right bearings, and ball screw nut.…”

Section: Methodsmentioning

confidence: 99%

“…S e is the sum of squares due to residual error, and S e has degrees of freedom as follows, f e =f T −3·4=12. V V , V P , V F are the average squares sum of operating conditions in deferent levels, and F i is the F check value [18]. r is the number of different levels, r=5.…”

Section: Influence Of Varying Operating Conditions On Temperature Risementioning

confidence: 99%

Temperature distribution and thermal error prediction of a CNC feed system under varying operating conditions

Jin

2014

Int J Adv Manuf Technol

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In high-speed and high-precision feed systems, the thermal errors are mainly caused by the non-uniform temperature variations and time-varying thermal deformations under different operating conditions. The research work undertaken herein ultimately aims to develop a generic method which is capable of evaluating the temperature rises of heat sources (such as supporting bearings and ball screw nut) and thermal positioning error of the feed system induced by varying operating conditions (feed speed, cutting load, and preload of ball screw). Experiments were carried out on a high-speed feed system experimental bench, and the influences of operating conditions on temperature rises of supporting bearings and ball screw nut were analyzed. Based on wavelet neural network and NARMA-L2 model, the relationship between temperature rise of supporting bearings and operating conditions was established. Furthermore, with the temperature of ball screw nut set to be a moving heating source load, the temperature and thermal deformation distributions of ball screw under bearings and ball screw nut heat sources were simulated. The experiment and simulation results show that the recommended modeling method can be used to predict thermal positioning errors of feed system with good accuracy. The work described lays a solid foundation of thermal error prediction and compensation in a feed system under varying operating conditions.

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“…The empirical-based modeling method aims at thermal error compensation. The prediction models are established by statistical methods, such as regression [3,4] and neural network [5,6]. The dependent variables of the models are the thermal-induced displacements.…”

Section: Introductionmentioning

confidence: 99%

Thermal Error Modeling of the CNC Machine Tool Based on Data Fusion Method of Kalman Filter

Wang¹,

Li²,

Cai³

et al. 2017

Mathematical Problems in Engineering

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This paper presents a modeling methodology for the thermal error of machine tool. The temperatures predicted by modified lumped-mass method and the temperatures measured by sensors are fused by the data fusion method of Kalman filter. The fused temperatures, instead of the measured temperatures used in traditional methods, are applied to predict the thermal error. The genetic algorithm is implemented to optimize the parameters in modified lumped-mass method and the covariances in Kalman filter. The simulations indicate that the proposed method performs much better compared with the traditional method of MRA, in terms of prediction accuracy and robustness under a variety of operating conditions. A compensation system is developed based on the controlling system of Siemens 840D. Validated by the compensation experiment, the thermal error after compensation has been reduced dramatically.

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Thermal sensor selection for the thermal error modeling of machine tool based on the fuzzy clustering method

Cited by 46 publications

References 17 publications

A novel linear virtual temperature constructing method for thermal error modeling of machine tools

A novel linear virtual temperature constructing method for thermal error modeling of machine tools

Temperature distribution and thermal error prediction of a CNC feed system under varying operating conditions

Thermal Error Modeling of the CNC Machine Tool Based on Data Fusion Method of Kalman Filter

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