Real-time thermal error compensation on machine tools using improved BP neural network

Author(s):  
Xiaohong Ren ◽  
Yong Sun ◽  
Tianpeng Zhou ◽  
Weidong Xu ◽  
Yinggao Yue
2015 ◽  
Vol 740 ◽  
pp. 120-126
Author(s):  
Zhi Peng Zhang ◽  
Kang Liu ◽  
Feng Guo

In order to improve the process precision of the machine tool, further development of SVMR was achieved by QT Creator. Support vector machine was applied to the ARM11 development board, SVMR model was online trained and real-time predicted the values of machine tool thermal error. Compared with the widely used BP neural network, this method has the characteristics of high compensation precision and strong generalization ability. Experiment research has proved that the stronger effectiveness and higher accuracy using this method.


2020 ◽  
Vol 22 ◽  
pp. 2386-2396
Author(s):  
T. Narendra Reddy ◽  
V. Shanmugaraj ◽  
Prakash Vinod ◽  
S. Gopi Krishna

2013 ◽  
Vol 819 ◽  
pp. 76-80 ◽  
Author(s):  
Bo Yang ◽  
Yi Wang ◽  
Wen Li Yu ◽  
Xin Hua Yao ◽  
Jian Zhong Fu

Great efforts have been made to improve the accuracy of NC machine tools, within which thermal error compensation is one of the most efficient ways. A new thermal error compensation instrument which is based on thermal modal analysis for NC machine tools is introduced in this paper. OMRONsCJ2M-CPU11 is used as microcontroller, and SAILING TECHNOLOGYs STA-A08 temperature measuring modules as temperature transmitter. Through hardware and software design, high precision and stability can be achieved. By measuring several key points temperature and making use of a thermal error compensation theory, real-time thermal error compensation can be output to the machine tool, thus thermal error can be reduced.


2014 ◽  
Vol 75 (5-8) ◽  
pp. 933-946 ◽  
Author(s):  
Shibin Yin ◽  
Yin Guo ◽  
Yongjie Ren ◽  
Jigui Zhu ◽  
Shourui Yang ◽  
...  

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