01948nam a2200265 a 4500 vtls000052465 KUKTEM 20251114204444.0 110329t2010 my a f m 000 0 eng d THE0005762(Local) 201905101529 aida 201204091033 Fida 201110041129 Fida 201107140042 VLOAD 201103291641 ida UMP TA418.7 .A39 2010 rs Bc. Mohd Aizuddin Mat Alwi Optimization of surface roughness in milling by using response surface method (RSM) / Mohd Aizuddin Mat Alwi Kuantan, Pahang : UMP, 2010 xiii, 53 p. : ill. (some col.) ; 30 cm. + 1 CD-ROM Project paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2010 Bibliography : p. 51-52 Aluminium Alloys are attractive materials due to their unique high strength-weight ratio that is maintained at elevated temperatures and their exceptional corrosion resistance. Face mill is used as cutting tool for experiment in milling machine.So in this study, the optimum of surface roughness is optimize by using response surface method. The experiments were carried out using CNC milling machine. The experiment was run with 15 experiment test. All the data was analyzed by using Response Surface Method (RSM) and Neural Network (NN). The result have shown that the feed gave the more affect on the both prediction value of Ra compare to the cutting speed and depth of cut r as P-values is less than 0.05. From the prediction data that shown, the different between both software is smaller and the value is acceptable to get the optimum value of surface roughness. Surface roughness Response surfaces (Statistics) Machining 0 0 0 1 20000 20000 2019-09-04 0 TA418.7 .A39 2010 rs Bc. 0000054067 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 20000 20000 2019-09-04 0 CD 5049 | TA418.7 .A39 2010 rs Bc. 0000054068 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 2335 2341 VTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*6502*9992