Surface roughness of Ti6Al4V after heat treatment evaluated by artificial neural networks

ALTUĞ M., Erdem M., Ozay C., BOZKIR O.

MATERIALS TESTING, cilt.58, sa.3, ss.189-199, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 58 Sayı: 3
  • Basım Tarihi: 2016
  • Doi Numarası: 10.3139/120.110844
  • Dergi Adı: MATERIALS TESTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.189-199
  • Anahtar Kelimeler: Ti6Al4V, heat treatment, WEDM, surface roughness, artificial neural network, DISCHARGE MACHINING WEDM, MULTIOBJECTIVE OPTIMIZATION, MICROSTRUCTURAL EVOLUTION, PARAMETRIC OPTIMIZATION, CUTTING PARAMETERS, TI-6AL-4V ALLOY, EDM PARAMETERS, TOOL STEEL, WIRE, MACHINABILITY
  • İnönü Üniversitesi Adresli: Evet

Özet

The study examines how, using wire electrical discharge machining (WEDM), the microstructural, mechanical and conductivity characteristics of the titanium alloy Ti6Al4V are changed as a result of heat treatment and the effect they have on machinability. Scanning electron microscope (SEM), optical microscope and X-ray diffraction (XRD) examinations were performed to determine various characteristics and additionally related microhardness and conductivity measurements were conducted. L-18 Taquchi test design was performed with three levels and six different parameters to determine the effect of such alterations on its machinability using WEDM and post-processing surface roughness (Ra) values were determined. Micro-changes were ensured successfully by using heat treatments. Results obtained with the optimization technique of artificial neural network (ANN) presented minimum surface roughness. Values obtained by using response surface method along with this equation were completely comparable with those achieved in the experiments. The best surface roughness value was obtained from sample D which had a tempered martensite structure.