Tri-axial Time-frequency Analysis for Tool Failures Detection in Deep Twist Drilling Process

Premature tool failure in deep drilling reduces product quality. By analyzing the deep drilling process signals through time and frequency domains in tri-axial vibrations, the early conditions before tool failure can be detected. From the experimental data, vibration time domain signals were analyze...

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Main Authors:	M. H. S., Harun, M. F., Ghazali, A. R., Yusoff
Format:	Article
Language:	English
Published:	Elsevier 2016
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/6820/ http://umpir.ump.edu.my/id/eprint/6820/ http://umpir.ump.edu.my/id/eprint/6820/ http://umpir.ump.edu.my/id/eprint/6820/1/fkm-2016-razlan-Tri-axial%20time-frequency%20analysis.pdf

id	ump-6820
recordtype	eprints
spelling	ump-68202018-01-12T01:19:16Z http://umpir.ump.edu.my/id/eprint/6820/ Tri-axial Time-frequency Analysis for Tool Failures Detection in Deep Twist Drilling Process M. H. S., Harun M. F., Ghazali A. R., Yusoff TJ Mechanical engineering and machinery Premature tool failure in deep drilling reduces product quality. By analyzing the deep drilling process signals through time and frequency domains in tri-axial vibrations, the early conditions before tool failure can be detected. From the experimental data, vibration time domain signals were analyzed by short-time Fourier transform to detect the tool wear mechanism. Results showed that tool wear accelerated before failure as increasing feed rate and cutting speed were recognized in the y- and z-axes in time–frequency analysis. Elsevier 2016 Article PeerReviewed application/pdf en cc_by_nc_nd http://umpir.ump.edu.my/id/eprint/6820/1/fkm-2016-razlan-Tri-axial%20time-frequency%20analysis.pdf M. H. S., Harun and M. F., Ghazali and A. R., Yusoff (2016) Tri-axial Time-frequency Analysis for Tool Failures Detection in Deep Twist Drilling Process. Procedia CIRP, 46. pp. 508-511. ISSN 2212-8271 http://dx.doi.org/10.1016/j.procir.2016.03.128 DOI: 10.1016/j.procir.2016.03.128
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery M. H. S., Harun M. F., Ghazali A. R., Yusoff Tri-axial Time-frequency Analysis for Tool Failures Detection in Deep Twist Drilling Process
description	Premature tool failure in deep drilling reduces product quality. By analyzing the deep drilling process signals through time and frequency domains in tri-axial vibrations, the early conditions before tool failure can be detected. From the experimental data, vibration time domain signals were analyzed by short-time Fourier transform to detect the tool wear mechanism. Results showed that tool wear accelerated before failure as increasing feed rate and cutting speed were recognized in the y- and z-axes in time–frequency analysis.
format	Article
author	M. H. S., Harun M. F., Ghazali A. R., Yusoff
author_facet	M. H. S., Harun M. F., Ghazali A. R., Yusoff
author_sort	M. H. S., Harun
title	Tri-axial Time-frequency Analysis for Tool Failures Detection in Deep Twist Drilling Process
title_short	Tri-axial Time-frequency Analysis for Tool Failures Detection in Deep Twist Drilling Process
title_full	Tri-axial Time-frequency Analysis for Tool Failures Detection in Deep Twist Drilling Process
title_fullStr	Tri-axial Time-frequency Analysis for Tool Failures Detection in Deep Twist Drilling Process
title_full_unstemmed	Tri-axial Time-frequency Analysis for Tool Failures Detection in Deep Twist Drilling Process
title_sort	tri-axial time-frequency analysis for tool failures detection in deep twist drilling process
publisher	Elsevier
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/6820/ http://umpir.ump.edu.my/id/eprint/6820/ http://umpir.ump.edu.my/id/eprint/6820/ http://umpir.ump.edu.my/id/eprint/6820/1/fkm-2016-razlan-Tri-axial%20time-frequency%20analysis.pdf
first_indexed	2023-09-18T22:02:56Z
last_indexed	2023-09-18T22:02:56Z
_version_	1777414519138549760

Tri-axial Time-frequency Analysis for Tool Failures Detection in Deep Twist Drilling Process

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