Approaches for efficient tool condition monitoring based on support vector machine

Ph.D

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Bibliographic Details
Main Author:	SUN JIE
Other Authors:	MECHANICAL ENGINEERING
Format:	Theses and Dissertations
Language:	English
Published:	2010
Subjects:	tool condition monitoring, support vector machine, feature selection, acoustic emission, cutting force
Online Access:	http://scholarbank.nus.edu.sg/handle/10635/14608
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Institution:	National University of Singapore
Language:	English

id	sg-nus-scholar.10635-14608
record_format	dspace
spelling	sg-nus-scholar.10635-146082015-01-09T06:03:44Z Approaches for efficient tool condition monitoring based on support vector machine SUN JIE MECHANICAL ENGINEERING HONG GEOK SOON RAHMAN, MUSTAFIZUR tool condition monitoring, support vector machine, feature selection, acoustic emission, cutting force Ph.D DOCTOR OF PHILOSOPHY 2010-04-08T10:44:55Z 2010-04-08T10:44:55Z 2005-04-23 Thesis SUN JIE (2005-04-23). Approaches for efficient tool condition monitoring based on support vector machine. ScholarBank@NUS Repository. http://scholarbank.nus.edu.sg/handle/10635/14608 NOT_IN_WOS en
institution	National University of Singapore
building	NUS Library
country	Singapore
collection	ScholarBank@NUS
language	English
topic	tool condition monitoring, support vector machine, feature selection, acoustic emission, cutting force
spellingShingle	tool condition monitoring, support vector machine, feature selection, acoustic emission, cutting force SUN JIE Approaches for efficient tool condition monitoring based on support vector machine
description	Ph.D
author2	MECHANICAL ENGINEERING
author_facet	MECHANICAL ENGINEERING SUN JIE
format	Theses and Dissertations
author	SUN JIE
author_sort	SUN JIE
title	Approaches for efficient tool condition monitoring based on support vector machine
title_short	Approaches for efficient tool condition monitoring based on support vector machine
title_full	Approaches for efficient tool condition monitoring based on support vector machine
title_fullStr	Approaches for efficient tool condition monitoring based on support vector machine
title_full_unstemmed	Approaches for efficient tool condition monitoring based on support vector machine
title_sort	approaches for efficient tool condition monitoring based on support vector machine
publishDate	2010
url	http://scholarbank.nus.edu.sg/handle/10635/14608
_version_	1681079055644884992

Approaches for efficient tool condition monitoring based on support vector machine

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