EXPERIMENTAL DESIGN AND ARTIFICIAL NEURAL NETWORK MODEL FOR TURNING THE 50CrV4 (SAE 6150) ALLOY USING COATED CARBIDE/CERMET CUTTING TOOLS


ÖZKAN M., ULAŞ H. B. , BİLGİN M.

MATERIALI IN TEHNOLOGIJE, vol.48, no.2, pp.227-236, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 48 Issue: 2
  • Publication Date: 2014
  • Journal Name: MATERIALI IN TEHNOLOGIJE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.227-236
  • Keywords: turning operations, coated carbide/cermet cutting tools, cutting force, surface roughness, artificial neural network, SURFACE-ROUGHNESS, TUNGSTEN CARBIDE, MACHINABILITY, STEEL, WEAR, PARAMETERS, PERFORMANCE, INSERTS, SPEED
  • Hacettepe University Affiliated: Yes

Abstract

In 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 (F-a, F-r, and F-c) 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.