الفهرس 
CHAPTER (I) INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
In machining of composites, surface quality is one of the most commonly specified customer requirements in which the major indication of surface quality on machined parts is surface roughness. The machining experiments on GFRPCs are carried out on a vertical milling machine (End Milling) using four machining parameters. The factors considered were; cutting speed, depth of cut, feed rate, and fiber orientation .The objective of the current work have been study the effect of cutting conditions (theoretical and practical) and analyze the theoretical and practical results of surface roughness by using a developed model and optimize the cutting conditions to get the value of surface roughness of GFRPCs machined parts. The proposed approach combines the experimental study and numerical prediction of surface quality during end mill machining on the GFRPCs. A procedure has been developed to assess and optimize the chosen factors to attain minimum surface roughness . The results indicated that the developed model is suitable for prediction surface roughness in machining of GFRPCs composites. Analysis of delamination due to cutting conditions are discussed and analyzed. from the microstructure graphics of machined surfaces the cutting speed plays a vital role in the resultant roughness of the machined surface of GFRPCs .Predicted responses and desirability are discussed and analyzed. The results showed that; the surface roughness is highly depends on spindle speed followed by feed rate. Depth of cut has least effect on the produced surface roughness, The surface roughness improved with increasing cutting speed. Also, surface roughness varies non- linearly with feed rate influence in the range from 25 to 140 mm/min. The increase in feed rate from 25 to 50 mm/min decreases surface roughness, but when depth of cut change from 100 to 140 mm/min Abstract -IIleads to increase of Ra value. The value of surface roughness increases as depth of cut increase but the effect is less than any other parameter. The surface roughness varies non-linearly with the fiber orientation influence in the range from 00 to 900. The increase in fiber orientation angle from 00to 22.50 decreases surface roughness. But the increase of fiber orientation from67.50 to 900 lead to increases of Ra value and the fiber orientation is more significant parameter for the surface roughness. predicted The value of surface roughness using neural network is closely with the experimental results. The experimental results and the predicted values of (Ra) by using the developed model are indicating a good correlation. from the RSM model based on the experimental results, the predicted and measured values are quite close, which indicates that the developed model can be effectively used to predict the surface roughness of GFRP composites.