Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0890-6955(02)00264-X
Title: Thermal error measurement and modelling in machine tools. Part II. Hybrid Bayesian Network - Support vector machine model
Authors: Ramesh, R.
Mannan, M.A. 
Poo, A.N. 
Keerthi, S.S. 
Keywords: Bayesian networks
Classification
Support vector machines
Thermal error model
Issue Date: Mar-2003
Source: Ramesh, R., Mannan, M.A., Poo, A.N., Keerthi, S.S. (2003-03). Thermal error measurement and modelling in machine tools. Part II. Hybrid Bayesian Network - Support vector machine model. International Journal of Machine Tools and Manufacture 43 (4) : 405-419. ScholarBank@NUS Repository. https://doi.org/10.1016/S0890-6955(02)00264-X
Abstract: Prediction accuracy of machine tool thermal error significantly depends on the structure of the error model. Machine tool thermal error varies considerably depending upon the specific operating parameters adopted. Most error models developed thus far generally employ neural networks to map temperature data against thermal error. However, it is very important to account for the specific conditions as well within the model. This paper presents a hybrid Support Vector Machines (SVM)-Bayesian Network (BN) model that seeks to address this issue. The experimental data is first classified using a BN model with a rule-based system. Once the classification has been effected, the error is predicted using a SVM model. The hybrid thermal error model thus predicts the thermal error according to the specific operating conditions. This concept leads to a more generalised prediction model than the conventional method of directly mapping error and temperature irrespective of conditions. Such a model is especially useful in a production environment wherein the machine tools are subject to a variety of operating conditions. © 2002 Elsevier Science Ltd. All Rights Reserved.
Source Title: International Journal of Machine Tools and Manufacture
URI: http://scholarbank.nus.edu.sg/handle/10635/61551
ISSN: 08906955
DOI: 10.1016/S0890-6955(02)00264-X
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