Tool wear monitoring by emitted sound analysis using Hilbert Huang transform and competitive neural network

Turning is one of the important machining processes in manufacturing industries. Tools wear during turning, is one of the major problems which may lead to production loss and machine down time. An effective tool wear monitoring method is therefore required to minimise the above. The work done in thi...

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Bibliographic Details
Main Author:	Emerson Raja, Joseph
Format:	Thesis
Published:	2014
Subjects:	TK2000-2891 Dynamoelectric machinery and auxiliaries
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id	my-mmu-ep.6309
record_format	uketd_dc
spelling	my-mmu-ep.63092016-01-29T04:37:07Z Tool wear monitoring by emitted sound analysis using Hilbert Huang transform and competitive neural network 2014-02 Emerson Raja, Joseph TK2000-2891 Dynamoelectric machinery and auxiliaries Turning is one of the important machining processes in manufacturing industries. Tools wear during turning, is one of the major problems which may lead to production loss and machine down time. An effective tool wear monitoring method is therefore required to minimise the above. The work done in this thesis is related to the development of a new method for tool wear monitoring using tool-emitted sound signal in conjunction with newly reported Hilbert Huang Transform (HHT). In the proposed method, the condition of the cutting tool insert is monitored to classify its state into three different categories, namely, fresh, slightly worn and severely worn by analysing the tool-emitted audible sound. A trained competitive neural network is employed for this purpose. The network is trained by using the instantaneous amplitudes and instantaneous frequencies extracted from the tool-emitted sound using HHT. The novelty of the present work is the use of HHT to extract the instantaneous amplitudes and the frequencies of the tool-emitted sound to determine the condition of the cutting tool insert based on its flank wear. HHT is a recently developed signal processing technique more suitable for analysing nonstationary and nonlinear signals such as tool-emitted sound. 2014-02 Thesis http://shdl.mmu.edu.my/6309/ http://library.mmu.edu.my/diglib/onlinedb/dig_lib.php phd doctoral Multimedia University Faculty of Engineering and Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	TK2000-2891 Dynamoelectric machinery and auxiliaries
spellingShingle	TK2000-2891 Dynamoelectric machinery and auxiliaries Emerson Raja, Joseph Tool wear monitoring by emitted sound analysis using Hilbert Huang transform and competitive neural network
description	Turning is one of the important machining processes in manufacturing industries. Tools wear during turning, is one of the major problems which may lead to production loss and machine down time. An effective tool wear monitoring method is therefore required to minimise the above. The work done in this thesis is related to the development of a new method for tool wear monitoring using tool-emitted sound signal in conjunction with newly reported Hilbert Huang Transform (HHT). In the proposed method, the condition of the cutting tool insert is monitored to classify its state into three different categories, namely, fresh, slightly worn and severely worn by analysing the tool-emitted audible sound. A trained competitive neural network is employed for this purpose. The network is trained by using the instantaneous amplitudes and instantaneous frequencies extracted from the tool-emitted sound using HHT. The novelty of the present work is the use of HHT to extract the instantaneous amplitudes and the frequencies of the tool-emitted sound to determine the condition of the cutting tool insert based on its flank wear. HHT is a recently developed signal processing technique more suitable for analysing nonstationary and nonlinear signals such as tool-emitted sound.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Emerson Raja, Joseph
author_facet	Emerson Raja, Joseph
author_sort	Emerson Raja, Joseph
title	Tool wear monitoring by emitted sound analysis using Hilbert Huang transform and competitive neural network
title_short	Tool wear monitoring by emitted sound analysis using Hilbert Huang transform and competitive neural network
title_full	Tool wear monitoring by emitted sound analysis using Hilbert Huang transform and competitive neural network
title_fullStr	Tool wear monitoring by emitted sound analysis using Hilbert Huang transform and competitive neural network
title_full_unstemmed	Tool wear monitoring by emitted sound analysis using Hilbert Huang transform and competitive neural network
title_sort	tool wear monitoring by emitted sound analysis using hilbert huang transform and competitive neural network
granting_institution	Multimedia University
granting_department	Faculty of Engineering and Technology
publishDate	2014
_version_	1747829627024310272

Tool wear monitoring by emitted sound analysis using Hilbert Huang transform and competitive neural network

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