Temperature-sensitive point selection of thermal error model of CNC machining center

Miao, Enming; Gong, Ya-Yun; Lian-chun, Dang; Miao, Jing

doi:10.1007/s00170-014-6009-y

Cited by 29 publications

(7 citation statements)

References 13 publications

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“…The advantage of this strategy is that in the process of classification, the collinearity between temperature-sensitive points can be reduced, which is beneficial in improving the accuracy of subsequent modeling algorithms and thus has been widely applied. Based on this strategy, En-ming et al [18], and Miao et al [19] proposed the classification of the temperature measurement points using a fuzzy clustering algorithm and the selection of the temperature sensitive point having the largest correlation with thermal error from each class. This method has also been used in a large number of applications [20]- [28].…”

Section: ) ''Black Box'' Methodsmentioning

confidence: 99%

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Thermal Error Modeling for Machine Tools: Mechanistic Analysis and Solution for the Pseudocorrelation of Temperature-Sensitive Points

et al. 2020

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The temperature-sensitive point is the input variable of the thermal error compensation model of computer numerical control (CNC) machine tools. At present, the most commonly used selection method is to measure the multipoint temperature and thermal error of the machine tool synchronously and select several temperature measurement points with the highest thermal error correlation as temperature-sensitive points according to the measurement data. This study reveals that this method sometimes has obvious mis-selection and causes the model to fail. The reason is that the weak correlation temperature measurement point away from the machine tool heat source amplifies the volatility of the correlation evaluation result, owing to the small overall change. If the calculated result exceeds the true strong correlation temperature measurement point, it will be incorrectly selected as the temperature-sensitive point. This scenario has been termed herein as the pseudocorrelation problem. With the gradual popularization of thermal error compensation technology for CNC machine tools, pseudocorrelation will seriously affect the mass production pass rate. Therefore, the study analyzes and rigorously proves the mathematical mechanism of this problem, and the temperature measurement point preselection algorithm based on the correlation coefficient volatility determination factor (CCVDF) is proposed to eliminate potential pseudocorrelation temperature points before selecting temperature-sensitive points. After 1000 random simulation experiments, the failure rate of Z-direction thermal error modeling after preselection decreased from 5.1% to 0.5%, and the Y-direction error decreased from 33.7% to 1.5%. This algorithm can greatly improve the qualification rate of large-scale equipment of thermal error compensation technology. INDEX TERMS CNC machine tool, thermal error modeling, temperature-sensitive point, correlation, mathematical mechanism.

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Section: ) ''Black Box'' Methodsmentioning

confidence: 99%

“…First, all temperature measurement points are classified. Among them, the fuzzy clustering algorithm is the most common classification algorithm [18]- [28]. Then, the point with the strongest correlation with thermal error is selected from each category as the temperature-sensitive point.…”

Section: A Thermal Error Modeling Methods Based On the Classificationmentioning

confidence: 99%

Thermal Error Modeling for Machine Tools: Mechanistic Analysis and Solution for the Pseudocorrelation of Temperature-Sensitive Points

et al. 2020

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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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“…13 To overcome this problem, the key TMPs which can characterize the temperature field variation should be selected from all TMPs. [14][15][16][17] And, thermal error prediction models are built based on these key TMPs.…”

Section: Introductionmentioning

confidence: 99%

“…20 A comprehensive analysis method combining fuzzy clustering, Grey correlation and stepwise regression determination coefficient is proposed to select TMPs of a CNC machining center. 14 R pattern clustering based on stepwise regression analysis is employed to filter TMPs of the servo system. 21 The partial correlation analysis is used to select key TMPs of a CNC machine tool.…”

Section: Introductionmentioning

confidence: 99%

The selection of key temperature measurement points for thermal error modeling of heavy-duty computer numerical control machine tools with density peaks clustering

Zhou

Liu

et al. 2019

Advances in Mechanical Engineering

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Having great impacts on machining precision, thermal error is one of the main error sources for heavy-duty computer numerical control machine tools. Thermal error compensation using prediction models with temperature field is an effective way to improve machining precision of computer numerical control machine tools. The accuracy and robustness of thermal error prediction models depend considerably on the selection of temperature measurement points. Too many temperature measurement points will increase the complexity of thermal error prediction models and incur overfitting problems. To improve the complexity and performances of prediction models, a selection method of key temperature measurement points based on density peaks clustering is presented in this article. This method is able to cluster massive temperature measurement points quickly and select the key temperature measurement point which characterizes the common feature of each cluster automatically. It is verified on the ZK5540A heavy-duty computer numerical control gantry drilling machine tool. Six key temperature measurement points are selected from the total 222 temperature measurement points with this method. Then, the back propagation neural network optimized by genetic algorithm thermal error model with the six key temperature measurement points is built and the accuracy and robustness of the model are analyzed. The results show that the model has high prediction accuracy and strong robustness.

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Temperature-sensitive point selection of thermal error model of CNC machining center

Cited by 29 publications

References 13 publications

Thermal Error Modeling for Machine Tools: Mechanistic Analysis and Solution for the Pseudocorrelation of Temperature-Sensitive Points

Thermal Error Modeling for Machine Tools: Mechanistic Analysis and Solution for the Pseudocorrelation of Temperature-Sensitive Points

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

The selection of key temperature measurement points for thermal error modeling of heavy-duty computer numerical control machine tools with density peaks clustering

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