One of the most important requirements of part manufacturing is the surface quality. This is so because the most important part is meeting the specific requirements of customers. The surface roughness is a leading indicator of the quality of the machined surface Parts. In the present work in an experimental study to achieve by application of Taguchi method to investigate the effect of three parameters, which known as cutting speeds of (45, 90, and 135 m/min), feed rate of (0.1, 0.2, and 0.3 mm/rev), and cut depth of (0.05, 0.1, and 0.15 mm) on performance measure of surface roughness (Ra). Thus to determine the optimal levels and to analyze the cutting parameter’s effect on the surface finish values by employing different method of Orthogonal array, S/N ratio, analysis of variance (ANOVA). During our work two models for prediction have been used. The first one is known as the method of regression analysis, and the second is the method of Adaptive - Neural Network (ANN) relying on practical results. The achieved results show that the estimation and prediction ability of neural networks is better than the regression analysis. Experimental results confirmed with optimal levels of the machining parameters which are clarified by using Taguchi optimization method. Also, the indicated results of the Taguchi’s method show its ability to improve the process.

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Naphtha and kerosene are mixed with Iraqi heavy crude oil at different concentrations rounded between (3-12) wt.%, in order to reduce viscosity to enhance its followability. This research investigated drag reduction (%Dr) in heavy oil mixtures at different flow rates (2 to 10 m3/hr) in temperature range 20-50C. The experimental results proved that Naphtha offered 40% reduction in pressure drop. The Power law model was adopted in this study to empirically correlate fiction factor (f) and the percentage of drag reduction(%Dr) from experimental data for Reynolds number range (534– 14695) and the concentration range from 3 to12 wt.%.

This work explores the possibility of using Newtonian turbulence k−ϵ and k−ω models for modelling crude oil flow in pipelines with drag reduction agents. These models have been applied to predict the friction factor, pressure drop and the drag reduction percentage. The simulation results of both models were compared with six published experimental data for crude oil flow in pipes with different types of drag reduction agents. The velocity near the wall was determined using the log law line of Newtonian fluid equation and by changing the parameter ΔB to achieve an excellent agreement with experimental data. Simulated data for k−ϵ model shows better agreement with most experimental data than the k−ω turbulence model.