Analysis of thrust force in drilling B4C-reinforced aluminium alloy using genetic learning algorithm

TAŞKESEN A., ALDAŞ K., Ozkul I., Kutukde K., ZÜMRÜT Y.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, cilt.75, ss.237-245, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 75
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1007/s00170-014-6062-6
  • Dergi Adı: INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.237-245
  • Anahtar Kelimeler: Gene expression programming, Composite, Drilling, Thrust force, SURFACE-ROUGHNESS, CUTTING FORCES, TOOL WEAR, OPTIMIZATION, PREDICTION, PARAMETERS
  • Gazi Üniversitesi Adresli: Evet

Özet

This paper presents an analysis for the prediction of thrust force in drilling of aluminium-based composites, reinforced with boron-carbide B4C produced with the powder-metallurgy (PM) technique. The formulation was derived on experimental bases. The experiments were conducted with various cutting tools and parameters on conditions of dry machining in a computer numerical control (CNC) vertical machining centre. The thrust forces were obtained by measuring the forces between the drill bit and the work pieces during the experiments. In the experiments, particle fraction, feed rate, spindle speed and drill bit type were used as input parameters, and thrust force was the output data for the gene expression programming (GEP) software. Customizing for formulation in order to describe the problem was generated by GEP, and it was analysed from different perspectives and verified the reliability of equation.