Time domain analyis of acoustic emissions signal for milling process

This thesis is to investigate the machining surface roughness by using acoustic emission (AE) method. The objectives of this project is to acquire AE data of the experiment by operating milling process, to study the correlation of AE parameter with work piece surface roughness (R) and to cluster AE...

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Main Author:	Wan Shalamyza , Wan Husin
Format:	Undergraduates Project Papers
Language:	English
Published:	2012
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/6508/ http://umpir.ump.edu.my/id/eprint/6508/ http://umpir.ump.edu.my/id/eprint/6508/1/CD6888.pdf

id	ump-6508
recordtype	eprints
spelling	ump-65082015-03-03T09:30:46Z http://umpir.ump.edu.my/id/eprint/6508/ Time domain analyis of acoustic emissions signal for milling process Wan Shalamyza , Wan Husin TA Engineering (General). Civil engineering (General) This thesis is to investigate the machining surface roughness by using acoustic emission (AE) method. The objectives of this project is to acquire AE data of the experiment by operating milling process, to study the correlation of AE parameter with work piece surface roughness (R) and to cluster AE data by using time domain analysis such as global statistical parameter and clustering method for online machining condition monitoring. In order to done this experiment, there is method to be taken. Firstly is the experimental setup. Computational Numerical Control (CNC) milling machine will be use through this project conduct the face milling. Machining parameter set for depth of cut, cutting speed and feed rate. Surface roughness being measure by using perthometer. USBwin for AE Node used for data acquisition. The material used is Hayness 188 alloy and carbide-coated as the cutting tool. Before experiment is started, the AE system need to be tested by using pencil break test to check whether AE system can receive AE signals properly. When lead of pencil break, it will generate as equal as AE signals emit during experiment. For data analysis, AE signal can be cluster based on surface roughness. For clustering analysis, it is related to its signal properties. Method used to cluster the signals is global statistical parameter such as root mean square (RMS), skewnes, kurtosis, peak value and variance. Based on experiment data, the pattern of AE parameter with time domain analysis can be concluded by clustering method. The analysis shows that AE signals data can be cluster by global statistical parameter according to its surface roughness measurement. Between all global statistical parameter, we can see that peak value, RMS and variance can show the significant pattern on clustering. As the project is success, data collected surface roughness monitoring can be made and be used in industry. So, this method can be use as an alternative method in industry to decrease the time used and the cost needed. 2012 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6508/1/CD6888.pdf Wan Shalamyza , Wan Husin (2012) Time domain analyis of acoustic emissions signal for milling process. Faculty of Mechanical Engineering , Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:77929&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Wan Shalamyza , Wan Husin Time domain analyis of acoustic emissions signal for milling process
description	This thesis is to investigate the machining surface roughness by using acoustic emission (AE) method. The objectives of this project is to acquire AE data of the experiment by operating milling process, to study the correlation of AE parameter with work piece surface roughness (R) and to cluster AE data by using time domain analysis such as global statistical parameter and clustering method for online machining condition monitoring. In order to done this experiment, there is method to be taken. Firstly is the experimental setup. Computational Numerical Control (CNC) milling machine will be use through this project conduct the face milling. Machining parameter set for depth of cut, cutting speed and feed rate. Surface roughness being measure by using perthometer. USBwin for AE Node used for data acquisition. The material used is Hayness 188 alloy and carbide-coated as the cutting tool. Before experiment is started, the AE system need to be tested by using pencil break test to check whether AE system can receive AE signals properly. When lead of pencil break, it will generate as equal as AE signals emit during experiment. For data analysis, AE signal can be cluster based on surface roughness. For clustering analysis, it is related to its signal properties. Method used to cluster the signals is global statistical parameter such as root mean square (RMS), skewnes, kurtosis, peak value and variance. Based on experiment data, the pattern of AE parameter with time domain analysis can be concluded by clustering method. The analysis shows that AE signals data can be cluster by global statistical parameter according to its surface roughness measurement. Between all global statistical parameter, we can see that peak value, RMS and variance can show the significant pattern on clustering. As the project is success, data collected surface roughness monitoring can be made and be used in industry. So, this method can be use as an alternative method in industry to decrease the time used and the cost needed.
format	Undergraduates Project Papers
author	Wan Shalamyza , Wan Husin
author_facet	Wan Shalamyza , Wan Husin
author_sort	Wan Shalamyza , Wan Husin
title	Time domain analyis of acoustic emissions signal for milling process
title_short	Time domain analyis of acoustic emissions signal for milling process
title_full	Time domain analyis of acoustic emissions signal for milling process
title_fullStr	Time domain analyis of acoustic emissions signal for milling process
title_full_unstemmed	Time domain analyis of acoustic emissions signal for milling process
title_sort	time domain analyis of acoustic emissions signal for milling process
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/6508/ http://umpir.ump.edu.my/id/eprint/6508/ http://umpir.ump.edu.my/id/eprint/6508/1/CD6888.pdf
first_indexed	2023-09-18T22:02:19Z
last_indexed	2023-09-18T22:02:19Z
_version_	1777414480072802304

Time domain analyis of acoustic emissions signal for milling process

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