Thermal error measurement and modelling in machine tools. Part II. Hybrid Bayesian Network—support vector machine model

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 temperatu...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of machine tools & manufacture Vol. 43; no. 4; pp. 405 - 419
Main Authors Ramesh, R., Mannan, M.A., Poo, A.N., Keerthi, S.S.
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.03.2003
Elsevier
Subjects
Applied sciences
Bayesian networks
Classification
Exact sciences and technology
Mechanical engineering. Machine design
Support vector machines
Thermal error model
Support vector machines
Bayesian networks
Thermal error model
Classification
Bayes estimation
Thermal strain
Error estimation
Expert system
Machining
Knowledge base
Neural network
Experimental study
Modeling
Operating conditions
Machine tool
Vector support machine
Hybrid model
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0890-6955
1879-2170
DOI:10.1016/S0890-6955(02)00264-X