Analysis of thrust force in drilling B4C-reinforced aluminium alloy using genetic learning algorithm
Analysis of thrust force in drilling B4C-reinforced aluminium alloy using genetic learning algorithm
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DOI：
10.1007/s00170-014-6062-6
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
A. Taskesen;K. Aldas;İ. Özkul;Kenan Kütükde;Yavuz Zumrut
中科院分区：
文献类型：
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作者：
A. Taskesen;K. Aldas;İ. Özkul;Kenan Kütükde;Yavuz Zumrut
本文提出了一种预测钻削铝基复合材料时的推力的分析，用粉末冶金（PM）技术生产的B4C增强的碳化硼。该配方是在实验基础上推导出来的。实验进行了各种刀具和参数的条件下干加工的计算机数控（CNC）立式加工中心。在实验过程中，通过测量钻头与工件之间的力来获得推力。在实验中，颗粒分数，进给速度，主轴转速和钻头类型被用作输入参数，推力是基因表达式编程（GEP）软件的输出数据。利用GEP生成了描述问题的定制公式，并从不同角度进行了分析，验证了公式的可靠性。
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.