Machining surface roughness monitoring using acoustic emission method

This thesis is to investigate the machining surface roughness monitoring using acoustic emission method. The objective of this project is to collect the data acquisition of the experiment by operating milling process, to study the correlation of AE parameter with work piece surface quality by compar...

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Main Author:	Mohd Syazlan, Mohd Hatta
Format:	Undergraduates Project Papers
Language:	English
Published:	2010
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/1872/ http://umpir.ump.edu.my/id/eprint/1872/ http://umpir.ump.edu.my/id/eprint/1872/1/Mohd_Syazlan_Mohd_Hatta_%28_CD_4932_%29.pdf

id	ump-1872
recordtype	eprints
spelling	ump-18722015-03-03T07:53:18Z http://umpir.ump.edu.my/id/eprint/1872/ Machining surface roughness monitoring using acoustic emission method Mohd Syazlan, Mohd Hatta TA Engineering (General). Civil engineering (General) This thesis is to investigate the machining surface roughness monitoring using acoustic emission method. The objective of this project is to collect the data acquisition of the experiment by operating milling process, to study the correlation of AE parameter with work piece surface quality by comparing the AE signals with average roughness, Ra of the work piece’s surface measured by using Perthometer, and to develop algorithm for online machining condition monitoring. In order to done this experiment, there are several steps to be taken. Firstly is the experimental setup. Milling machine will be used through this project. Work piece will be placed on the table and the cutting tool will rotate continuously. AE sensor will be placed on the work piece, and adhesively bonded onto the surface of the work piece with grease applied between the specimen and the sensors. Before experiment is started, the AE system need to be tested first to check whether AE system can receive AE signals properly. This can be done by using pencil break test. When lead of pencil break, it will generate as equal signal as AE signal emitted during experiment. Then, the experiment is run using carbide coated cutting tool and Aluminium alloy for work piece. Machine parameter use will be varies to get different pattern of AE signal, as different value of surface roughness also will be collected. From all data collected (energy, counts, power spectrum density, Ra), algorithm for surface roughness monitoring can be made and be used in industry. Based on experiment data, the pattern of AE parameter with Fast Fourier Transform, FFT can be concluded. For smooth surface, Ra value in range of 0.643 μm – 0.879 μm will have the frequency below 1 Hz and amplitude is high only at that value. For the rough surface, Ra value in range of 2.833 μm – 3.004 μm will have the frequency from 1 Hz to 2 Hz and amplitude is high continuously at that value. In term of count, smooth surface will generate counts in range of 20 000 to 30 000 and rough surface will counts 30 000 and above. 2010-12 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1872/1/Mohd_Syazlan_Mohd_Hatta_%28_CD_4932_%29.pdf Mohd Syazlan, Mohd Hatta (2010) Machining surface roughness monitoring using acoustic emission method. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:51877&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) Mohd Syazlan, Mohd Hatta Machining surface roughness monitoring using acoustic emission method
description	This thesis is to investigate the machining surface roughness monitoring using acoustic emission method. The objective of this project is to collect the data acquisition of the experiment by operating milling process, to study the correlation of AE parameter with work piece surface quality by comparing the AE signals with average roughness, Ra of the work piece’s surface measured by using Perthometer, and to develop algorithm for online machining condition monitoring. In order to done this experiment, there are several steps to be taken. Firstly is the experimental setup. Milling machine will be used through this project. Work piece will be placed on the table and the cutting tool will rotate continuously. AE sensor will be placed on the work piece, and adhesively bonded onto the surface of the work piece with grease applied between the specimen and the sensors. Before experiment is started, the AE system need to be tested first to check whether AE system can receive AE signals properly. This can be done by using pencil break test. When lead of pencil break, it will generate as equal signal as AE signal emitted during experiment. Then, the experiment is run using carbide coated cutting tool and Aluminium alloy for work piece. Machine parameter use will be varies to get different pattern of AE signal, as different value of surface roughness also will be collected. From all data collected (energy, counts, power spectrum density, Ra), algorithm for surface roughness monitoring can be made and be used in industry. Based on experiment data, the pattern of AE parameter with Fast Fourier Transform, FFT can be concluded. For smooth surface, Ra value in range of 0.643 μm – 0.879 μm will have the frequency below 1 Hz and amplitude is high only at that value. For the rough surface, Ra value in range of 2.833 μm – 3.004 μm will have the frequency from 1 Hz to 2 Hz and amplitude is high continuously at that value. In term of count, smooth surface will generate counts in range of 20 000 to 30 000 and rough surface will counts 30 000 and above.
format	Undergraduates Project Papers
author	Mohd Syazlan, Mohd Hatta
author_facet	Mohd Syazlan, Mohd Hatta
author_sort	Mohd Syazlan, Mohd Hatta
title	Machining surface roughness monitoring using acoustic emission method
title_short	Machining surface roughness monitoring using acoustic emission method
title_full	Machining surface roughness monitoring using acoustic emission method
title_fullStr	Machining surface roughness monitoring using acoustic emission method
title_full_unstemmed	Machining surface roughness monitoring using acoustic emission method
title_sort	machining surface roughness monitoring using acoustic emission method
publishDate	2010
url	http://umpir.ump.edu.my/id/eprint/1872/ http://umpir.ump.edu.my/id/eprint/1872/ http://umpir.ump.edu.my/id/eprint/1872/1/Mohd_Syazlan_Mohd_Hatta_%28_CD_4932_%29.pdf
first_indexed	2023-09-18T21:55:10Z
last_indexed	2023-09-18T21:55:10Z
_version_	1777414030082703360

Machining surface roughness monitoring using acoustic emission method

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