03196nam a2200277 a 4500 vtls000051877 KUKTEM 20251114204504.0 110314t2010 my a f m 000 0 eng d THE0005836(Local) 201905131609 aida 201107140037 VLOAD 201103140939 ida UMP TA418.84 .S93 2010 rs Bc. Mohd Syazlan Mohd Hatta Machining surface roughness monitoring using acoustic emission method / Mohd Syazlan Mohd Hatta Machining surface roughness monitoring using acoustic emission method [electronic resource] Kuantan, Pahang : UMP, 2010 xv, 67 p. : ill. (some col.) ; 30 cm.+ 1 computer disc Project paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2010 Bibliography : p. 63-64 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. Surface roughness Accoustic emission Accoustic emission testing 0 0 0 1 20000 20000 2019-09-04 0 TA418.84 .S93 2010 rs Bc. 0000053833 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 20000 20000 2019-09-04 0 CD 4932 | TA418.84 .S93 2010 rs Bc. 0000053834 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 2907 2913 VTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5040*5200*6500*6501*6502*9992