Robustness Analysis of the Thermal Error Model for a CNC Machine Tool

Author(s):  
Xinpeng Zhu ◽  
Quan Liu ◽  
Xiaomei Zhang ◽  
Xuemei Jiang ◽  
Ping Lou
2013 ◽  
Vol 303-306 ◽  
pp. 1782-1785
Author(s):  
Chong Zhi Mao ◽  
Qian Jian Guo

The purpose of this research is to improve the machining accuracy of a CNC machine tool through thermal error modeling and compensation. In this paper, a thermal error model based on back propagation networks (BPN) is presented, and the compensation is fulfilled. The results show that the BPN model improves the prediction accuracy of thermal errors on the CNC machine tool, and the thermal drift has been reduced from 15 to 5 after compensation.


2010 ◽  
Vol 135 ◽  
pp. 170-173 ◽  
Author(s):  
Qian Jian Guo ◽  
Jian Guo Yang

Four key temperature points of a CNC machine tool were obtained in this paper, and a thermal error model based on the four key temperature points was proposed by using based back propagation neural network. A thermal error compensation system was developed based on the proposed model, and which has been applied to the CNC machine tool in daily production. The results show that the thermal error in workpiece diameter has been reduced from 33 to 6 .


2014 ◽  
Vol 3 (2) ◽  
pp. 113 ◽  
Author(s):  
Ramesh Babu ◽  
V.Prabhu Raja ◽  
J. Kanchana ◽  
Devara Venkata Krishna

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.


2010 ◽  
Vol 455 ◽  
pp. 621-624
Author(s):  
X. Li ◽  
Y.Y. Yu

Because of the practical requirement of real-time collection and analysis of CNC machine tool processing status information, we discuss the necessity and feasibility of applying ubiquitous sensor network(USN) in CNC machine tools by analyzing the characteristics of ubiquitous sensor network and the development trend of CNC machine tools, and application of machine tool thermal error compensation based on USN is presented.


2016 ◽  
Vol 24 (10) ◽  
pp. 2480-2489 ◽  
Author(s):  
苗恩铭 MIAO En-ming ◽  
刘 义 LIU Yi ◽  
董云飞 DONG Yun-fei ◽  
陈维康 CHEN Wei-kang

2015 ◽  
Vol 23 (5) ◽  
pp. 1401-1408
Author(s):  
何振亚 HE Zhen-ya ◽  
傅建中 FU Jian-zhong ◽  
陈子辰 CHEN Zi-chen

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