Thermal Error Compensation on Machine Tools Using Rough Set Artificial Neural Networks

This paper is a study of the application of rough set artificial neural networks to the problem of calculating thermal error compensation values for axis positioning on a machine tool. The primary focus is on the development of a rough set approach to reduce a thermal error compensation system which...

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Bibliographic Details
Published in2009 WRI World Congress on Computer Science and Information Engineering Vol. 5; pp. 51 - 55
Main Authors Huanglin Zeng, Yong Sun, Haiyan Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2009
Subjects
artificial neural network
Artificial neural networks
Control systems
Error compensation
Heat engines
machine tool
Machine tools
Machining
optimal modeling
rough set
Solar heating
Temperature
Thermal engineering
thermal error compensation
Thermal variables control
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Summary:This paper is a study of the application of rough set artificial neural networks to the problem of calculating thermal error compensation values for axis positioning on a machine tool. The primary focus is on the development of a rough set approach to reduce a thermal error compensation system which is composed of all of the temperature variables. One modeling of thermal error compensation on machine tools is presented by way of using artificial neural networks integrated rough sets. Positioning error compensation capabilities were tested using industry standard equipment and procedures, and the results obtained is validated for applicability to the problem.
ISBN:9780769535074
0769535070
DOI:10.1109/CSIE.2009.155