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

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.

2009 ◽  
Vol 16-19 ◽  
pp. 410-414 ◽  
Author(s):  
Chang Long Zhao ◽  
Yi Qiang Wang ◽  
Xue Song Guan

In this paper, a hybrid method of correlation analysis based on the gray theory and the least squares support vector machine is proposed to model the thermal error of spindle of NC machine tool and predict the thermal error. The gray correlation analysis is used to optimize the measuring points of spindle. The optimum measuring points and the measured thermal error of spindle are regarded as the data to be trained to build the thermal error prediction model based on the least squares support vector machine (LS-SVM). The results show that the thermal error prediction model based on LS-SVM of NC machine tool has advantages of high precision and good generalization performance. The prediction model can be used in real-time compensation of NC machine tool and can prove the process precision and reduce cost.


2009 ◽  
Vol 626-627 ◽  
pp. 135-140 ◽  
Author(s):  
Qian Jian Guo ◽  
X.N. Qi

Through analysis of the thermal errors affected NC machine tool, a new prediction model based on BP neural networks is presented, and ant colony algorithm is applied to train the weights of neural network model. Finally, thermal error compensation experiment is implemented, and the thermal error is reduced from 35μm to 6μm. The result shows that the local minimum problem of BP neural network is overcome, and the model accuracy is improved.


2013 ◽  
Vol 744 ◽  
pp. 147-152
Author(s):  
Zi Jian Liu ◽  
Zhi Min Yu ◽  
Si Ming Li ◽  
Yan Di Ai

For the degree of thermal deformation nonlinear is high and difficult to predict, fuzzy neural network modeling (FNN) based on Takagi-Sugeno model was applied to the NC machine tool thermal error modeling thus the complete thermal error fuzzy neural network mathematical model on NC machine tool was established and network parameters initialization and learning method were discussed. Thermal error experiment was conducted on large NC gantry rail grinder spindle box system and two independent groups of spindle thermal error data were collected, one was used to establish thermal error fuzzy neural network prediction model and another one was used to verify the prediction accuracy of this model. The test results show that fuzzy neural network model has high prediction accuracy.


2003 ◽  
Vol 125 (2) ◽  
pp. 245-254 ◽  
Author(s):  
Hong Yang ◽  
Jun Ni

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.


2006 ◽  
Vol 532-533 ◽  
pp. 849-852
Author(s):  
Yan Li ◽  
Da Peng Fan

Based on the analysis of geometric errors of mechanism assembling, this paper applies the multi-body system kinematics theory in building the orientation error model of three-axis turntable induced by assembling errors. The results using the model could be easily applied in controlling turntable by error’s compensation, and shows which assembling error item has the larger effect. Furthermore,referenced by the measuring means of three-axis turntable, this paper conceives the definitions of the volumetric and orientation accuracy aimed at the turntable system, enlightened from the conception of NC machine tool. This research may be helpful to the foundation of the systematic conception of the turntable accuracy no matter in theory or in measuring method.


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