الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Taguchi method has been used to identify the optimal combination of influential factors in the end milling process. The experiments have been performed on aluminum silicon (Al-Si) alloy reinforced by aluminum oxide nanoparticles. A Taguchi orthogonal array L27 was selected for various combinations of different controllable factors (number of flutes of end mill, volume fraction of nanoparticles,
spindle speed and feed rate). The process responses, i.e. surface roughness (Ra), material removal rate (MRR), flatness error and temperature are measured and recorded for each experiment. The results are analyzed by Taguchi S/N ratio and then the optimal combination of controllable factors and their contributions are identified. The results showed that, feed rate was the most significant parameter on surface roughness with contribution 71.608%. Feed rate was the most significant
parameter on MRR with a contribution of 100%. The most significant parameter on
flatness error was number of flutes with a contribution of 62.59%. The feed rate has the highest contribution on temperature with contribution of 44.657%. from the Genetics Algorithm (GA) the optimization of results obtained based on the regression equation of Taguchi techniques as follows; surface roughness (Ra=
1.1094 µm), flatness error (F= 0.01043 µm) and temperature (T = 35.682 C). The
predicted results possess an average accuracy from Taguchi method of 100% in the case of material removal rate, 84.61 % in the case of surface roughness, 90.68% in flatness error and accuracy of temperature of 92.76 % .