Thermal error modeling of machine tool based on fuzzy c-means cluster analysis and minimal-resource allocating networks

2011 ◽  
Vol 60 (5-8) ◽  
pp. 463-472 ◽  
Author(s):  
Jian Han ◽  
Liping Wang ◽  
Ningbo Cheng ◽  
Haitong Wang
Keyword(s):  
Cluster Analysis ◽  
Machine Tool ◽  
Thermal Error ◽  
Error Modeling ◽  
Thermal Error Modeling ◽  
Fuzzy C Means ◽  
Minimal Resource

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.

Thermal Error Modeling of a CNC Machine Tool

2013 ◽  
Vol 303-306 ◽  
pp. 1782-1785
Author(s):  
Chong Zhi Mao ◽  
Qian Jian Guo
Keyword(s):  
Machine Tool ◽  
Back Propagation ◽  
Thermal Error ◽  
Error Modeling ◽  
Machining Accuracy ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Thermal Error Modeling ◽  
Thermal Errors ◽  
Thermal Error Model

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.

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