Surface roughness during the turning process of a 50CrV4 (SAE 6150) steel and ANN based modeling


MATERIALS TESTING, vol.57, no.10, pp.889-896, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 57 Issue: 10
  • Publication Date: 2015
  • Doi Number: 10.3139/120.110793
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.889-896
  • Keywords: Turning, cutting force, surface roughness, artificial neural network, statistical analysis, CUTTING PARAMETERS, NEURAL-NETWORK, TOOL STEELS, MACHINABILITY, CARBIDE, WEAR
  • Gazi University Affiliated: Yes


This study presents experimental and ANN modelling work to determine machining parameters and achieve better surface roughness in turning operation using coated and uncoated cermet cutting inserts (CCCT and UCCT). 50CrV4 (SAE 6150) material (Brinell hardness (HB) 311) was machined on CNC lathe. Processing parameters were determined using experimental design techniques. Cutting speed, feed rate, depth of cut, tip radius and type of cutting inserts were defined as turning processes parameters. During the machining processes cutting forces and then surface roughness were measured. Multiple regression and ANOVA analysis were performed and significant process parameters defined. An ANN model was also developed on the basis of experimental study results. The model is used for prediction of surface roughness and cutting forces achieving a very close agreement with experimental results.