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

ÖZKAN M. T.

MATERIALS TESTING, cilt.57, sa.10, ss.889-896, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 57 Sayı: 10
  • Basım Tarihi: 2015
  • Doi Numarası: 10.3139/120.110793
  • Dergi Adı: MATERIALS TESTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.889-896
  • Anahtar Kelimeler: Turning, cutting force, surface roughness, artificial neural network, statistical analysis, CUTTING PARAMETERS, NEURAL-NETWORK, TOOL STEELS, MACHINABILITY, CARBIDE, WEAR
  • Gazi Üniversitesi Adresli: Evet

Özet

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.