Modeling of NC Machine Tool Thermal Error Based on Adaptive Best-fitting WLS-SVM

2009 ◽  
Vol 45 (03) ◽  
pp. 178 ◽  
Author(s):  
Weiqing LIN
Keyword(s):  
Machine Tool ◽  
Thermal Error ◽  
Nc Machine Tool ◽  
Nc Machine ◽  
Best Fitting

The Thermal Error Prediction of NC Machine Tool Based on LS-SVM and Grey Theory

2009 ◽  
Vol 16-19 ◽  
pp. 410-414 ◽  
Author(s):  
Chang Long Zhao ◽  
Yi Qiang Wang ◽  
Xue Song Guan
Keyword(s):  
Support Vector Machine ◽  
Correlation Analysis ◽  
Prediction Model ◽  
Machine Tool ◽  
Least Squares ◽  
Thermal Error ◽  
Support Vector ◽  
Error Prediction ◽  
Nc Machine Tool ◽  
Nc Machine

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.

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.

Application and Study of Fuzzy Neural Network Theory Based on Takagi-Sugeno Model to Thermal Error Modeling on NC Machine Tool

2013 ◽  
Vol 744 ◽  
pp. 147-152
Author(s):  
Zi Jian Liu ◽  
Zhi Min Yu ◽  
Si Ming Li ◽  
Yan Di Ai
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Fuzzy Neural Network ◽  
Thermal Error ◽  
Error Modeling ◽  
Nc Machine Tool ◽  
Fuzzy Neural ◽  
Nc Machine ◽  
Sugeno Model ◽  
Takagi Sugeno

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.

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.

Model Research on Special Metal Cutting NC Machine Tool

2007 ◽  
Vol 10-12 ◽  
pp. 806-811
Author(s):  
Tong Zhao ◽  
P.Q. Ye ◽  
H. Zhang ◽  
X.K. Wang
Keyword(s):  
Machine Tool ◽  
Metal Cutting ◽  
Relation Module ◽  
Nc Machine Tool ◽  
Model Research ◽  
Unit Model ◽  
Proposed Model ◽  
Control Research ◽  
Function Unit ◽  
Nc Machine

In this paper the model of special metal cutting NC machine Tool is presented, which consists of a base module, an overall control module, particular functional modules as well as a relation module. Each module involved in aforementioned model will be composed by software, hardware and mechanical parts, so as to combine the convergence of the ideas of modularization and mechanical-electrical integration into current understanding of special NC machine tool through the proposed model. Specially, the relation module is introduced to deal with the linking among all the other modules. The presented model aims to broaden the perspective of machine designers intending to increase the efficiency in machine design. By giving the so-called function unit model a novel modeling approach is delivered to carry out control research of special metal cutting NC machine, which is followed by the formalization description method presented as a possible abstraction methodology towards the efficient description and identification of special metal cutting NC machine tool.

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