This paper is focused on studying the effect of cutting parameters (spindle speed, feed and depth of cut) on the response (temperature and tool life) during turning process. The inserts used in this study are carbide inserts coated with TiAlN (Titanum, Aluminium and Nitride) for machining a shaft of stainless steel 316L. Finite difference method was used to find the temperature distribution. The experimental results were done using infrared camera while the simulation process was performed using Matlab software package. The results showed that the  maximum difference between the experimental and simulation results was equal to 19.3 , so, a good agreement between the experimental and simulation results  was achieved. Tool life was decreased when spindle speed and feed were increased.

Electric discharge machine (EDM) or may be call electric spark machine is one of most important of cutting process or manufacturing process because it give high accurate dimension and can be product most complex shape. In this present material removal rate (MRR) and electrode wear rate (EWR) for tool steel AISI L2 was study. The input parametric for this process is current, pulse on time and pulse off time. Full factorial method is used to formulate machine parameters and find the optimal process parameters of electric spark. The result show that the MRR is increasing with increasing in current and pulse on time while EWR is decreasing when current and pulse on time is increase. The experimented and predicted values of this process are approximately equal.

Electric discharge machine is one of the most important non-traditional cutting processes conducted without contact between the workpiece and tool electrode. Each cutting process was associated with residual stresses, and these stresses are significant in determining life and product performance. This study aimed to determine the residual stress produced in the electric discharge machine (EDM) using the X-ray diffraction method. The used EDM parameters in this study are current Ip (10, 20, 30) A, pulse on-time Ton (50, 100, 150) μs, and pulse off-time Toff (6.5, 12,25) μs these parametric divided into 27 specimens. Full factorial was used to analyze the result using Minilab 17 software. The result showed that approximately between the experimentally and predict result. Also, the result illustrated that the residual stress was increasing with increases in each parametric EDM used. Maximum tensile residual stress is (838.86 Mpa) at a higher value of machine parameters, while the best residual stress achieved is compressive residual stress at low machine parameters, and it reaches 201 Mpa.

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This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.