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

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Abstract

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.

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Authors and Affiliations

  1. Department of Manufacturing Engineering, Gazi University, 06500, Teknikokullar, Ankara, Turkey

    Ahmet Taşkesen

  2. Department of Mechanical Engineering, Faculty of Engineering, Aksaray University, 68100, Aksaray, Turkey

    Kemal Aldas & İskender Özkul

  3. Institute of Science and Technology, Gazi University, 06500, Teknikokullar, Ankara, Turkey

    Kenan Kütükde

  4. Department of Mechanical Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey

    Yavuz Zumrut

Authors
  1. Ahmet Taşkesen
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  2. Kemal Aldas
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  3. İskender Özkul
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  4. Kenan Kütükde
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  5. Yavuz Zumrut
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Corresponding author

Correspondence to İskender Özkul.

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Taşkesen, A., Aldas, K., Özkul, İ. et al. Analysis of thrust force in drilling B4C-reinforced aluminium alloy using genetic learning algorithm. Int J Adv Manuf Technol 75, 237–245 (2014). https://doi.org/10.1007/s00170-014-6062-6

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  • DOI: https://doi.org/10.1007/s00170-014-6062-6

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