02235nam a2200277 a 4500 vtls000052554 KUKTEM 20251114204447.0 110331t2010 my a f m 000 0 eng d THE0005785(Local) 201905101625 aida 201110041141 Fida 201107140042 VLOAD 201103311720 ida 201103311639 ida UMP TA418.7 .R89 2010 rs Bc. Ruzaimi Zainon Optimization of surface roughness in milling using neural network (NN) / Ruzaimi Zainon Optimization of surface roughness in milling using neural network (NN) [computer file] Kuantan, Pahang : UMP, 2010 xv, 74 p. : ill. (some col.) ; 30 cm. + 1 computer disc Project paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2010 Bibliography : p. 63-64 This thesis discuss the Optimization of surface roughness in milling using Artificial Neural Network (ANN).Response Surface Methodology (RSM) and Neural Network implemented to model the end milling process that are using coated carbide TiN as the cutting tool and aluminium 6061 as material due to predict the resulting of surface roughness. The parameters of the variables are feed, cutting speed and depth of cut while the output is surface roughness. The model is validated through a comparison of the experimental values with their predicted counterparts. A good agreement is found where RSM approaches show 83.64% accuracy which reliable to be use in Ra prediction and state the feed parameter is the most significant parameter followed by depth of cut and cutting speed influence the surface roughness. ANN technique shows 96.68% of accuracy which is feasible and applicable in the prediction value of Ra. The proved technique opens the door for a new, simple and efficient approach that could be applied to the calibration of other empirical models of machining. Surface roughness Response surfaces (Statistics) Machining 0 0 0 1 20000 20000 2019-09-04 0 CD 5067 | TA418.7 .R89 2010 rs Bc. 0000054104 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 20000 20000 2019-09-04 0 TA418.7 .R89 2010 rs Bc. 0000054103 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 2414 2420 VTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5040*5200*6500*6501*6502*9992