01939nam a2200229 a 4500001001400000003000700014005001700021008004100038020002200079040000800101100001900109245009300128246009200221260003400313300006300347502012300410504002800533520107700561650002201638650003501660650001401695vtls000052554KUKTEM20251114204447.0110331t2010    my a   f m    000 0 eng d  aTHE0005785(Local)  aUMP0 aRuzaimi Zainon10aOptimization of surface roughness in milling using neural network (NN) /cRuzaimi Zainon3 aOptimization of surface roughness in milling using neural network (NN)h[computer file]  aKuantan, Pahang :bUMP,c2010  axv, 74 p. :bill. (some col.) ;c30 cm. +e1 computer disc  aProject paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2010  aBibliography : p. 63-643 aThis 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. 0aSurface roughness 0aResponse surfaces (Statistics) 0aMachining