2026-10-19T01:52:43Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/158142026-10-18T20:30:39Zopenairedrivercom_ebyu_10000col_ebyu_10001

Experimental design and artificial neural network model for turning the 50CrV4 (SAE 6150) alloy using coated carbide/Cermet cutting toolsOzkan, Murat TolgaUlas, Hasan BasriBilgin, MusaArtificial neural networkCoated carbide/cermet cutting toolsCutting forceSurface roughnessTurning operationsIn this experimental study, the 50CrV4 (SAE 6150) steel was subjected to the machining tests with coated carbide and cermet cutting tools in a turning operation. The tests were carried out at various cutting speeds, feed rates and cutting depths. In the light of these parameters, cutting forces and surface-roughness values were determined. Three components (Fa, Fr and Fc) of the cutting forces were measured during the tests using a dynamometer, while the machined surface-roughness values were determined using a surface roughness measuring unit. A multiple regression analysis and experimental design were performed statistically. The measured surface-roughness values were used for the modeling with an artificial neural network system (ANNS). The relations between the cutting forces and the surface-roughness values were also defined.Institute of Metals Technology2014Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/158141580-2949enMateriali in Tehnologijeinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/15814