A thermal error model for large machine tools that considers environmental thermal hysteresis effects

2014 ◽  
Vol 82-83 ◽  
pp. 11-20 ◽  
Author(s):  
Bo Tan ◽  
Xinyong Mao ◽  
Hongqi Liu ◽  
Bin Li ◽  
Songping He ◽  
...  
Keyword(s):  
Machine Tools ◽  
Thermal Hysteresis ◽  
Thermal Error ◽  
Error Model ◽  
Thermal Error Model ◽  
Large Machine

scholarly journals Identification, development and testing of thermal error compensation model for a headstock assembly of CNC turning centre

2014 ◽  
Vol 3 (2) ◽  
pp. 113 ◽  
Author(s):  
Ramesh Babu ◽  
V.Prabhu Raja ◽  
J. Kanchana ◽  
Devara Venkata Krishna
Keyword(s):  
Machine Tool ◽  
Analysis Of Variance ◽  
Error Compensation ◽  
Machine Tools ◽  
Thermal Error ◽  
Error Model ◽  
Transient Temperature ◽  
Cnc Machine ◽  
Grey Correlation ◽  
Thermal Error Model

In CNC machine tools, transient temperature variation in the headstock assembly is the major contributors for spindle thermal error. The compensation of thermal error is critical for ensuring the accuracy of machine tool. The performance of an error compensation system depends largely on the accuracy and robustness of the thermal error model. In the present work, a robust thermal error model is developed for minimizing the error in lateral direction of the spindle which significantly influences the geometrical accuracy of the workpiece. Analysis-of-variance (ANOVA) is applied to the results of the experiments in determining the percentage contribution of each individual temperature key point against a stated level of confidence. Based on the analysis of existing approaches for thermal error modeling of machine tools, an approach of LASSO (least absolute shrinkage and selection operator) is proposed in order to avoid the multi collinearity problem. The proposed method is an innovative variable selection method to remove redundant or unimportant temperature key points in the linear thermal error model and minimize the residual sum of squares. The predictive error model is found to have better robustness and accuracy in comparison to the combination of grey correlation and step wise linear regression for error compensation of CNC lathe. Keywords: Analysis Of Variance (ANOVA), CNC Machine Tool, Grey Correlation Analysis (GCA), Headstock Assembly, LASSO Regression, Mean Absolute Deviation (MAD), Mean Square Error (MSE), Robustness, Standard Deviation (SD), Thermal Error.

scholarly journals Thermal Error Prediction of Ball Screws Based on PSO-LSTM

2021 ◽  
Author(s):  
Xiangsheng Gao ◽  
Yueyang Guo ◽  
Dzonu Ambrose Hanson ◽  
Zhihao Liu ◽  
Min Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
High Speed ◽  
Thermal Characteristic ◽  
Thermal Error ◽  
Error Model ◽  
Cnc Machine Tools ◽  
Error Prediction ◽  
Model Based ◽  
Thermal Error Model

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.

Machine-Tool Error Observer Design With Application to Thermal Error Tracking

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Hua-Wei Ko ◽  
Patrick Bazzoli ◽  
J. Adam Nisbett ◽  
Douglas Bristow ◽  
Yujie Chen ◽  
...  
Keyword(s):  
Machine Tool ◽  
Parameter Identification ◽  
Thermal Error ◽  
Error Model ◽  
Optimal Designs ◽  
Model Parameters ◽  
Volumetric Error ◽  
Identification Procedure ◽  
Five Axis ◽  
Large Machine

Abstract A parameter identification procedure for identifying the parameters of a volumetric error model of a large machine tool requires hundreds of random volumetric error components in its workspace and thus takes hours of measurement time. It causes thermal errors of a large machine difficult to be tracked and compensated periodically. This paper demonstrates the application of the optimal observation design theories to volumetric error model parameter identification of a large five-axis machine. Optimal designs maximize the amount of information carried in the observations. In this paper, K-optimal designs are applied for the construction of machine-tool error observers by determining locations in the workspace at which 80 components of volumetric errors to be measured so that the model parameters can be identified in 5% of an 8-h shift. Many of optimal designs tend to localize observations at the boundary of the workspace. This leaves large volumes of the workspace inadequately represented, making the identified model inadequate. Therefore, the constrained optimization algorithms that force the distribution of observation points in the machine’s workspace are developed. Optimal designs reduce the number of observations in the identification procedure. This opens up the possibility of tracking thermal variations of the volumetric error model with periodic measurements. The design, implementation, and performance of a constrained K-optimal in tracking the thermal variations of the volumetric error over a 400-min period of operation are also reported. About 70–80% of machine-tool error can be explained using the proposed thermal error modeling methodology.

Clustering Regression Modeling for Gear Hobbing Machine Thermal Error Compensation

2009 ◽  
Vol 416 ◽  
pp. 401-405
Author(s):  
Qian Jian Guo ◽  
Xiao Ni Qi
Keyword(s):  
Error Compensation ◽  
Thermal Error ◽  
Regression Modeling ◽  
Error Model ◽  
Error Modeling ◽  
Machining Accuracy ◽  
Thermal Error Modeling ◽  
Gear Hobbing ◽  
Thermal Error Model ◽  
Thermal Error Compensation

This paper proposes a new thermal error modeling methodology called Clustering Regression Thermal Error Modeling which not only improves the accuracy and robustness but also saves the time and cost of gear hobbing machine thermal error model. The major heat sources causing poor machining accuracy of gear hobbing machine are investigated. Clustering analysis method is applied to reduce the number of temperature sensors. Least squares regression modeling approach is used to build thermal error model for thermal error on-line prediction of gear hobbing machine. Model performance evaluation through thermal error compensation experiments shows that the new methodology has the advantage of higher accuracy and robustness.

scholarly journals Study on Temperature Measurement Point Optimization and Thermal Error Modeling of NC Machine Tools

2017 ◽  
Vol 11 (1) ◽  
pp. 37-43 ◽  
Author(s):  
Shuo Fan ◽  
Qianjian Guo
Keyword(s):  
Machine Tool ◽  
Temperature Measurement ◽  
Thermal Error ◽  
Error Model ◽  
Measurement Point ◽  
Nc Machine Tool ◽  
Temperature Measurement Point ◽  
Nc Machine ◽  
Thermal Error Model ◽  
Selection Of

Background: In precision machining, thermal error is the main source of machine tool error. And thermal error compensation is an effective method to reduce thermal error. Objective: In order to improve the prediction accuracy and computational efficiency of thermal error model, a new optimization method used for the selection of temperature measurement point is proposed. Method: This method is based on stepwise regression. According to the results of partial-F statistic, new variable is selected one by one, unapparent variables are deleted, and optimization selection of temperature measurement point is fulfilled, thermal error model of the NC machine tool is presented. Result: The new modeling method was used on NC machine tool, which reduced the temperature point number from 24 to 5. Moreover, model residual was less than 5µm after compensation. Conclusion: The result shows that the new thermal error model has higher prediction accuracy and less temperature variables.

A Combined Error Model for Thermal Error Compensation of Machine Tools

2013 ◽  
Vol 820 ◽  
pp. 147-150 ◽  
Author(s):  
Wei Wang ◽  
Jian Guo Yang
Keyword(s):  
Error Compensation ◽  
Machine Tools ◽  
Thermal Error ◽  
Industrial Applications ◽  
Error Model ◽  
Cnc Machine ◽  
Different Temperatures ◽  
Experimental Validations ◽  
Compensation Approach ◽  
Thermal Error Compensation

In this paper, a combined error model for thermal error compensation of machine tools is presented. Through the analysis of thermal error data of machine spindle at different temperatures, the error variation law is obtained. Experiments on the axial directional spindle deformation on a CNC machine center are conducted to build and validate the proposed models. The experimental validations show that the thermal errors of the machine tool are reduced effectively after applying the error compensation approach. The combined error model performs better than the traditional time series and neural network model in terms of prediction accuracy and robustness, which means that the new model is more suitable for complex working conditions in industrial applications.

Dynamic Modeling for Machine Tool Thermal Error Compensation

2003 ◽  
Vol 125 (2) ◽  
pp. 245-254 ◽  
Author(s):  
Hong Yang ◽  
Jun Ni
Keyword(s):  
Machine Tool ◽  
Model Performance ◽  
Thermal Error ◽  
Error Model ◽  
Error Modeling ◽  
Thermal Error Modeling ◽  
Modeling Methodology ◽  
On Line ◽  
Thermal Error Model ◽  
Thermal Error Compensation

This paper proposes a new thermal error modeling methodology called the Dynamic Thermal Error Modeling which improves the accuracy and robustness of the machine tool thermal error model. The characteristics of the thermoelastic system are investigated from the dynamic system viewpoint. The pseudo-hysteresis effect is revealed to be the major factor causing poor robustness of the conventional static thermal error model. System identification theory is applied to build the dynamic thermal error model for machine tool thermal error on-line prediction. The modeling procedure for the linear Output Error (OE) model is illustrated using simulation work for both one-dimensional spindle and two-dimensional machine structure thermal deformations. Model performance evaluation through spindle experiments shows that the thermal error dynamic model has advantages over the conventional static model in terms of model accuracy and robustness.

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