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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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2010
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| 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 |
| Summary: | 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. |
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