Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0890-6955(02)00264-X
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dc.titleThermal error measurement and modelling in machine tools. Part II. Hybrid Bayesian Network - Support vector machine model
dc.contributor.authorRamesh, R.
dc.contributor.authorMannan, M.A.
dc.contributor.authorPoo, A.N.
dc.contributor.authorKeerthi, S.S.
dc.date.accessioned2014-06-17T06:36:30Z
dc.date.available2014-06-17T06:36:30Z
dc.date.issued2003-03
dc.identifier.citationRamesh, 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
dc.identifier.issn08906955
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/61551
dc.description.abstractPrediction 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0890-6955(02)00264-X
dc.sourceScopus
dc.subjectBayesian networks
dc.subjectClassification
dc.subjectSupport vector machines
dc.subjectThermal error model
dc.typeArticle
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1016/S0890-6955(02)00264-X
dc.description.sourcetitleInternational Journal of Machine Tools and Manufacture
dc.description.volume43
dc.description.issue4
dc.description.page405-419
dc.description.codenIMTME
dc.identifier.isiut000180944600009
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