000			02972nam a2200241 a 4500
001			vtls000077929
003			KUKTEM
005			20251114204602.0
008			140506t2012 my a m 000 0 eng d
020			_aTHE0005835(Local)
039		9	_a201905131606 _baida _c201407111029 _dsaini _c201405061047 _dtraining3 _y201405061045 _ztraining3
040			_aUMP
090			_aTA418.84 .S535 2012 rs Bc.
100	0		_aWan Shalamyza Wan Husin
245	1	0	_aTime domain analyis of acoustic emissions signal for milling process / _cWan Shalamyza Wan Husin
260			_aKuantan, Pahang : _bUMP, _c2012
300			_axIv, 53 p. : _bill. (some col.) ; _c30 cm. + _e1 CD-ROM
502			_aProject paper (Bachelor of Mechanical Engineering ) -- Universiti Malaysia Pahang - 2012
504			_aBibliography : p. 50-53
520	3		_aThis 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.
650		0	_aAcoustic emission
999			_aVIRTUA40 _c4575 _d4581
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*9992