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003 KUKTEM
005 20251114204444.0
008 100524t2009 my dao f m 000 0 eng d
020 _aTHE0005784(Local)
039 9 _a201905101622
_baida
_c201107140042
_dVLOAD
_c201103301001
_dida
_y201005240931
_zida
040 _aUMP
090 _aTA418.7 .R69 2009 rs Bc.
100 0 _aMohd. Rozaidy Afzan Vitalis
245 1 0 _aPrediction of surface roughness in wire electric discharge machining (EDM) of aluminum alloy based on experimental results /
_cMohd. Rozaidy Afzan Bin Vitalis
246 3 _aPrediction of surface roughness in wire electric discharge machining (EDM) of aluminum alloy based on experimental results
_h[electronic resource]
260 _aKuantan, Pahang :
_bUMP,
_c2009
300 _axiv, 59 p. :
_bill. ;
_c30 cm. +
_e1 computer disc
502 _aProject paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2009
520 3 _aSurface roughness is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. The aim of this research is to develop first order and second order prediction mathematical model for Surface roughness using response surface methodology (RSM) when machining using Wire-EDM for aluminum alloy 6061-T6 and compare both mathematical modeling to find the most effective prediction model. SODICK AQ353L machine was used to cut Aluminum Alloy 6061-T6 and PERTHOMETER to measure surface roughness. By using Response Surface Method (RSM) of experiment, first and second order models were developed with 95% confidence level. The machine parameters that had been considered in this study are ON-time, OFF-time, peak discharge current and wire speed. It was established that the surface roughness is most influenced by On-time. The percentage error of surface roughness predicted is calculated to obtain the accuracy of mathematical model build, the second order prediction model gives less percentage error which is 3.29% compare to first order prediction model 3.68%. So, the second order mathematical modeling is more suitable for prediction of surface roughness.
650 0 _aSurface roughness
650 0 _aMachining
999 _aVIRTUA40
_c2344
_d2350
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*6501*9992