000			03173nam a2200253 a 4500
001			vtls000075640
003			KUKTEM
005			20251114204553.0
008			131018t2012 my da f m 000 0 eng d
020			_aTHE0006653(Local)
039		9	_a201905161550 _bamirul _c201704261209 _dida _c201311251650 _dnabilah _y201310181212 _znabilah
040			_aUMP
090			_aTJ1296 .S93 2012 rs Bc.
100	0		_aMohd Syah Waliyullah Ad-Dahlawi Mat Razali
245	1	0	_aOptimization of abrasive machining of ductile cast iron using water based ZnO nanoparticles : _ba support vector machine approach / _cMohd Syah Waliyullah Ad-Dahlawi Mat Razali
260			_aKuantan, Pahang : _bUMP, _c2012
300			_axv, 53 p. : _bill. ; _c30 cm. + _e1 CD-ROM
502			_aProject paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang – 2012
504			_aBibliography : p. 52-53
520	3		_aThis project presents the optimization of abrasive machining of ductile cast iron using water based ZnO nanoparticles. This study were carried out to investigate the performance of grinding machine of ductile cast iron based on response surface methodology (RSM), to develop optimization model for grinding parameters using support vector machine (SVM) and to investigate the effect of water based ZnO nanoparticles in grinding machine. Analysis of variance has been carried out to check the adequacy of the experimental results. The mathematical modeling has been developed using response surface methodology to investigate the performance of grinding machine of ductile cast iron. The optimization model of grinding parameter was developed and the effect of water based ZnO nanoparticles was investigated. From the obtained results, the optimum parameter for grinding model is 30m/min table speed and 40μm depth of cut. The quality of product was determined by output criteria that are minimum temperature rise, minimum surface roughness and maximum material removal rate. Based on prediction data from RSM shows that 2nd order gives the good performance of grinding machine with the significant p-value of analysis of variance that is below than 0.05 and support with R-square value nearly 0.99. Based on the support vector machine (SVM) results, high depth of cut and low table speed gives high quality of product. It shows that SVM result is acceptable since the results was the same as obtained results from response surface methodology (RSM) and can be used to optimize the grinding machine. The results also shows that water based ZnO nanoparticles as a nanocoolant give impact to the temperature rise. It gives temperature rise almost zero compared to conventional coolant. High temperature rise will affect the surface roughness of product, so that it is very efficiency to choose water based ZnOnano particles as a nanocoolant. As the conclusion, the results obtained from this project can be used to optimize the precision grinding machine to get high quality of product using water based ZnO nanoparticles.
650		0	_aAbrasives
650		0	_aCast-iron
999			_aVIRTUA40 _c4300 _d4306
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*9992