Machine tool thermal error modeling and prediction by grey neural network

2011 ◽  
Vol 59 (9-12) ◽  
pp. 1065-1072 ◽  
Author(s):  
Yi Zhang ◽  
Jianguo Yang ◽  
Hui Jiang
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Thermal Error ◽  
Error Modeling ◽  
Thermal Error Modeling ◽  
Modeling And Prediction

Thermal Error Modeling and Prediction of a Heavy Floor-Type Milling and Boring Machine Tool

2015 ◽  
Author(s):  
Fengchun Li ◽  
Tiemin Li ◽  
Haitong Wang
Keyword(s):  
Boundary Conditions ◽  
Machine Tool ◽  
Superposition Principle ◽  
Engineering Application ◽  
Thermal Error ◽  
Error Modeling ◽  
Simulation Data ◽  
Thermal Error Modeling ◽  
Fea Model ◽  
Modeling And Prediction

Thermal error modeling and prediction of a heavy floor-type milling and boring machine tool was studied in this paper. An FEA model and a thermal network of the machine tool’s ram was established. The influence of boundary conditions on thermal error was studied to find out the boundary conditions that needn’t to be calculated precisely, reducing the time cost of the work. Superposition principle of heat sources was used in the FEA to get the simulation data of thermal error and temperature. A model based on the simulation data was established to predict the thermal error during the work process. An experiment was performed to verify the accuracy of the model. The result shows that the model accuracy is 87%. The method in this paper is expected to be used in engineering application.

Application of Partial Least Squares Neural Network in Thermal Error Modeling for CNC Machine Tool

2008 ◽  
Vol 392-394 ◽  
pp. 30-34 ◽  
Author(s):  
J.H. Shen ◽  
Jian Guo Yang
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Least Squares ◽  
Partial Least Squares ◽  
Thermal Error ◽  
Error Modeling ◽  
Neural Network Modeling ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Thermal Error Modeling

This paper presents a partial least squares neural network modeling method for CNC machine tool thermal errors. This method uses the neural network learning rule to obtain the PLS parameters instead of the traditional linear method in partial least squares regression so as to overcome the multicollinearity and nonlinearity problem in thermal error modeling. The basic principle and architecture of PLSNN is described and the new method is applied on the thermal error modeling for a CNC turning center. After model validation with two groups of new testing data and performance comparison with other five different modeling methods, PLSNN performs better than the others with better robustness.

Research on Thermal Error Modeling of NC Machine Tool Based on BP Neural Networks

2009 ◽  
Vol 626-627 ◽  
pp. 135-140 ◽  
Author(s):  
Qian Jian Guo ◽  
X.N. Qi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Machine Tool ◽  
Ant Colony Algorithm ◽  
Thermal Error ◽  
Error Modeling ◽  
Minimum Problem ◽  
Bp Neural Networks ◽  
Nc Machine Tool ◽  
Nc Machine

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.

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