Please use this identifier to cite or link to this item: https://doi.org/10.1007/s001700200132
Title: Support vector machines model for classification of thermal error in machine tools
Authors: Ramesh, R.
Mannan, M.A. 
Poo, A.N. 
Keywords: Artificial neural networks
Error compensation
Open-architecture controller
Support vector machines
Thermal error
Issue Date: 2002
Source: Ramesh, R., Mannan, M.A., Poo, A.N. (2002). Support vector machines model for classification of thermal error in machine tools. International Journal of Advanced Manufacturing Technology 20 (2) : 114-120. ScholarBank@NUS Repository. https://doi.org/10.1007/s001700200132
Abstract: This paper addresses a change in the concept of machine tool thermal error prediction which has been hitherto carried out by directly mapping them with the temperature of critical elements on the machine. The model developed herein using support vector machines, a powerful data-training algorithm, seeks to account for the impact of specific operating conditions, in addition to temperature variation, on the effective prediction of thermal errors. Several experiments were conducted to study the error pattern, which was found to change significantly with variation in operating conditions. This model attempts to classify the error based on operating conditions. Once classified, the error is then predicted based on the temperature states. This paper also briefly describes the concept of the implementation of such a comprehensive model along with an on-line error assessment and calibration system in a PC-based open-architecture controller environment, so that it could be employed in regular production for the purpose of periodic calibration of machine tools.
Source Title: International Journal of Advanced Manufacturing Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/61421
ISSN: 02683768
DOI: 10.1007/s001700200132
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