Performance of coated and uncoated carbide/cermet cutting tools during turning

ULAŞ H. B., BİLGİN M., SEZER H. K., ÖZKAN M. T.

MATERIALS TESTING, cilt.60, sa.9, ss.893-901, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 60 Sayı: 9
  • Basım Tarihi: 2018
  • Doi Numarası: 10.3139/120.111228
  • Dergi Adı: MATERIALS TESTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.893-901
  • Anahtar Kelimeler: Turning operations, carbide/cermet cutting tools, cutting force, surface roughness, artificial neural network, ARTIFICIAL NEURAL-NETWORK, SURFACE-ROUGHNESS, SAE 6150, TUNGSTEN CARBIDE, MACHINABILITY, STEEL, WEAR, PARAMETERS, SPEED, PREDICTION
  • Gazi Üniversitesi Adresli: Evet

Özet

Historically, cutting force and surface roughness are known to be important performance indicators in conventional machining operations and are mainly affected by material type and the choice of cutting tool. One well-known method to improve cutting tool performance is covering these tools with durable ceramic coatings to protect them from wear and thermal degradation. This work elucidates the advantage of Al2O3 and TiN coatings and presents important performance improvements in turning operation. Process parameters such as cutting speed, feed rate, cutting depth and tip radius were taken into consideration in a total of 540 experiments. The design of the experiment and a statistical analysis were performed to reveal significant process parameters. A special experimental setup was designed to measure in-situ cutting forces. The surface roughness of the machined surfaces was measured. An artificial neural network model was developed to predict optimum performance parameters.