Turning processes investigation of materials austenitic, martensitic and duplex stainless steels and prediction of cutting forces using artificial neural network (ANN) techniques

ULAŞ H. B., ÖZKAN M. T.

INDIAN JOURNAL OF ENGINEERING AND MATERIALS SCIENCES, cilt.26, sa.2, ss.93-104, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26 Sayı: 2
  • Basım Tarihi: 2019
  • Dergi Adı: INDIAN JOURNAL OF ENGINEERING AND MATERIALS SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.93-104
  • Anahtar Kelimeler: Stainless steels, Machining, Surface roughness, Cutting force, Artificial neural networks (ANN), SURFACE-ROUGHNESS, SAE 6150, TOOL WEAR, MACHINABILITY, PERFORMANCE
  • Gazi Üniversitesi Adresli: Evet

Özet

In this study focus on the performance of machining parameters that are cutting forces and surface roughness when the turning processes AISI 304 (Austenitic), AISI 420 (Martensitic) and AISI 2205 (Duplex) stainless steels have been explored the machinability performance and cutting forces. The machining tests have been conducted on a CNC turning center using coated cemented carbide tools. Machining parameters have been chosen cutting speeds (120, 150, 180 and 210 m/min), feed rate (0.1 mm/rev) and depth of cut (1 mm/rev) according to cutting tool manufacturer recommendation catalog. Machining forces and surface roughness variables have been measured when the turning processes. It has also been investigated the worn of cutting tools and explored under the scanning electron microscope (SEM). An ANN model has been developed using experimental results. Experimental results and ANN model results have been compared with each other. It seemed that cutting forces have been modeled using ANN techniques and ANN results have been very close to experimental results.