Turning is the most popular machining operation. The quality of the product may be determined using a variety of metrics, such as the surface generation method and the surface roughness of the product. This work uses cutting variables to obtain the best surface quality through a mathematical model. The suggested surface generation in this work results from deriving it using the Bezier technique, with degree (5th) having six chosen control points. One of the critical indicators of the quality of machined components is the surface roughness created during the machining process. Surface roughness improvement via machining process parameter optimization has been extensively researched. The Taguchi Method and actual tests were employed for evaluating the surface quality of complicated forms; regression models with three different variables for the cutting process, such as cutting speed, depth of cut, and feed rate, were also used. According to the experimental findings, the most significant effect of feed rate on the surface roughness is approximately (40.9%), and the more minor effect of depth of cut on the surface roughness is almost (16.23%). In addition, the average percentage error is 4.93%, the maximum error is 0.14 mm, and the minimum error is -0.143 mm for the prediction using the regression equation.
The most common type of abrasive water jet is known as a valuable and advanced non-traditional machining operation due to its no heat-affected zone, best in removing material, very environmentally friendly, and no mechanical stresses. This paper gives an idea about Abrasive water jets in terms of applications, advantages, and limitations. Also illustrates the influence of the parameters on the material removal rate. The effect of feed rate, pressure, and stand-off distance were worked, at three levels for material removal rate (MRR) to machining Aluminium alloy type-5083 by using a tool consisting of a mixture of 70% water and 30% abrasives of red garnet. The distance of the standoff has the most significant impact on the rate of material removal, which is subsequently followed by the feed rate and finally the pressure. The findings demonstrated that the Taguchi model is capable of making accurate predictions regarding the machining reactions, with a rate of material removal of 93.3%.