An application of Neural Networks for Prediction of Surface Texture Parameters in Turning

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 turn...

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Bibliographic Details
Published inInternational Journal of Neural Networks and Advanced Applications Vol. 9; pp. 18 - 22
Main Authors Karagiannis, S., Stavropoulos, P., Kechagias, J.
Format Journal Article
LanguageEnglish
Published 11.03.2022
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Summary: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.
ISSN:2313-0563
2313-0563
DOI:10.46300/91016.2022.9.4