A new thermal error modeling method for CNC machine tools

Han, Jian; Wang, Liping; Wang, Haitong; Cheng, Ningbo

doi:10.1007/s00170-011-3796-2

Cited by 66 publications

(18 citation statements)

References 21 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%

“…The weighted coefficient of T 1 is 0.438, and that of T 4 is 0.562. To verify the effectiveness of the virtual measurement point, the multiple linear regression model is used to compare the forecast effect of the different input variables [7]. Therefore, the virtual temperature T 14 structured by T 1 and T 4 should be T 14 ¼ 0:438*T 1 þ 0:562*T 4 :…”

Section: Application Example 31 Selection Of Temperature Measurementmentioning

confidence: 99%

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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%

Section: Application Example 31 Selection Of Temperature Measurementmentioning

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

View full text Add to dashboard Cite

show abstract

“…These disturbances can be compensated in part, for instance by employing temperature compensation and a compensation of tool wear by respective measurements or predictions [4][5][6][7][8][9][10]. However, in order to guarantee deviations from the desired shape below 1 μm, shape measurements are still required.…”

Section: State-of-the-artmentioning

confidence: 99%

Optical, in situ, three-dimensional, absolute shape measurements in CNC metal working lathes

Kuschmierz

Davids

Metschke

et al. 2016

Int J Adv Manuf Technol

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Temperature drifts, tool deterioration, unknown vibrations, as well as spindle play are major effects which decrease the achievable precision of CNC lathes and lead to shape deviations of the processed workpieces. Since currently no measurement system exists for precise, in situ, 3D shape monitoring, much effort is required to simulate and compensate these effects. In this article, we propose an optical measurement principle for first part quality manufacturing. The absolute shape, meaning the diameter and surface profile, of the workpiece is determined from its tangential velocity and the surface distance. In order to allow keyhole access, both measurands are determined simultaneously with a single sensor. Measurements inside a metal working lathe show that the standard uncertainty for the absolute shape measurement is below 1 μm. The measurement uncertainty is nearly independent of the lateral surface velocity and roughness. The in situ measurements are compared to measurements with a tactile coordinate measurement machine for reference.

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“…Due to the increasing speed and precision of machining, thermal deformation is becoming the main factor that influences the machining accuracy of precision machine tools (Han et al, 2012;Mayr et al, 2012). Up to 75% of the overall geometrical errors of machined workpieces can be induced by the effects of temperature (Mayr et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A novel method for fast identification of a machine tool selected point temperature rise based on an adaptive unscented Kalman filter

Xia

et al. 2014

J. Zhejiang Univ. Sci. A

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Abstract:A novel method is presented for fast identification of a machine tool selected point temperature rise, based on an adaptive unscented Kalman filter. The major advantage of the method is its ability to predict the selected point temperature rise in a short period of measuring time, like 30 min, instead of 3 to 6 h in conventional temperature rise tests. A fast identification algorithm is proposed to predict the selected point temperature rise and the steady-state temperature. An adaptive law is applied to adjust parameters dynamically by the actual measured temperature, which can effectively avoid the failure of prediction. A vertical machining center was used to validate the effectiveness of the presented method. Taking any selected point, we could identify the temperature rise at that point in 28 min. However, if the method was not used, it took 394 min to obtain the temperature rise curve from the start-up of the machine tool to the time when it reached a steady-state temperature. The root mean square error (RMSE) between the estimated and measured temperatures in the period of 394 min was 0.1291 °C, and the error between the estimated and measured steady-state temperatures was 0.097 °C. Therefore, this method can effectively and quickly identify a machine tool selected point temperature rise.

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A new thermal error modeling method for CNC machine tools

Cited by 66 publications

References 21 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

Optical, in situ, three-dimensional, absolute shape measurements in CNC metal working lathes

A novel method for fast identification of a machine tool selected point temperature rise based on an adaptive unscented Kalman filter

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