Abstract
Surface roughness, an indicator of surface quality is one of the most specified customer requirements in a machining process. Mastering of surface quality issues helps avoiding failure, enhances component integrity, and reduces overall costs. Copper alloy (GCCuSn12) surface quality, achieved in turning, constitutes the subject of the current research study. Test specimens in the form of near-to-net-shape bars and a titanium nitride coated cemented carbide (T30) cutting tool were used. The independent variables considered were the tool nose radius (r), feed rate (f), cutting speed (V), and depth of cut (a). Process performance is estimated using the statistical surface texture parameters Rα, Ry, and Rz. To predict the surface roughness, an artificial feed forward back propagation neural network (ANN) model was designed for the data obtained.
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