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采用BP神经网络预测碳纤维增强树脂基复合材料的钻削力

刘洋 李鹏南 陈明 唐思文 邱新义

刘洋, 李鹏南, 陈明, 唐思文, 邱新义. 采用BP神经网络预测碳纤维增强树脂基复合材料的钻削力[J]. 机械科学与技术, 2017, 36(4): 586-591. doi: 10.13433/j.cnki.1003-8728.2017.0415
引用本文: 刘洋, 李鹏南, 陈明, 唐思文, 邱新义. 采用BP神经网络预测碳纤维增强树脂基复合材料的钻削力[J]. 机械科学与技术, 2017, 36(4): 586-591. doi: 10.13433/j.cnki.1003-8728.2017.0415
Liu Yang, Li Pengnan, Chen Ming, Tang Siwen, Qiu Xinyi. Prediction of Drilling Force in Drilling Process of CFRP via Back Propagation Neural Network[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(4): 586-591. doi: 10.13433/j.cnki.1003-8728.2017.0415
Citation: Liu Yang, Li Pengnan, Chen Ming, Tang Siwen, Qiu Xinyi. Prediction of Drilling Force in Drilling Process of CFRP via Back Propagation Neural Network[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(4): 586-591. doi: 10.13433/j.cnki.1003-8728.2017.0415

采用BP神经网络预测碳纤维增强树脂基复合材料的钻削力

doi: 10.13433/j.cnki.1003-8728.2017.0415
基金项目: 

国家自然科学基金项目(51275168)与国家科技重大专项项目(2012ZX04003031)资助

详细信息
    作者简介:

    刘洋(1990-),硕士研究生,研究方向为碳纤维复合材料制孔技术研究工作、计算机图形学,18711346265@163.com

    通讯作者:

    李鹏南(联系人),教授,硕士生导师,2002lpn@163.com

Prediction of Drilling Force in Drilling Process of CFRP via Back Propagation Neural Network

  • 摘要: 采用双锋角钻头对碳纤维复合材料进行钻削试验,基于反向传播算法的人工神经网络建立钻削轴向力与主轴转速、进给速度之间的非线性关系模型,对比分析三种不同第二主切削刃与第一主切削刃之比的双锋角钻头在试验加工参数下钻削轴向力变化规律。结果表明:与多元线性回归预测模型对比,在相同试验数据为基础的预测计算下,BP神经网络预测值相对误差明显减小,网络预测值误差均在3%之内,而多元线性回归模型最大误差值达到了12.46%,BP神经网络能建立更精准轴向力预测模型。从降低钻削轴向力的角度分析,应采用第二主切削刃与第一主切削刃之比为1的双锋角钻头进行钻削加工。
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出版历程
  • 收稿日期:  2015-10-09
  • 刊出日期:  2017-04-05

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