Optimization of Machining Conditions for Surface Quality in Milling AA7039-Based Metal Matrix Composites

KARABULUT Ş., Gokmen U., Cinici H.

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, cilt.43, sa.3, ss.1071-1082, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 43 Sayı: 3
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1007/s13369-017-2691-z
  • Dergi Adı: ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1071-1082
  • Anahtar Kelimeler: AA7039/B4C/SiC, Metal matrix composite, Milling, Surface roughness, ANN, Regression analysis, RESPONSE-SURFACE, CUTTING FORCE, TOOL WEAR, ROUGHNESS, PARAMETERS, MACHINABILITY, MMC
  • Hacettepe Üniversitesi Adresli: Evet

Özet

In the present study, aluminium 7039-based 10% weight fraction of SiC and 10% metal matrix composites (MMCs) were produced by powder metallurgy and investigated the influential machining parameters on surface quality using an uncoated carbide tool under dry cutting environment. The experiments were performed based on Taguchi's ( with a mixed orthogonal array. The optimal cutting parameters for better surface finish were defined using signal-to-noise (S / N) ratio, central composite desirability function and regression analysis. Experimental results showed that the finished surface was significantly affected by the interfacial bonding effect of reinforcement particles and built-up edge formation. Better surface roughness was obtained in the milling of AA7039/-MMCs. The analysis findings indicated that the most significant cutting parameters on the finished surface were the cutting speed and feed rate. The cutting depth was not shown to have a meaningful correlation with surface quality in the milling of both MMCs. Artificial neural network was produced a low prediction error as compared to the regression modelling.