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磨料水射流铣削钛合金深度与表面粗糙度研究

杜航 熊杰 陈炜 李登 巫世晶

杜航, 熊杰, 陈炜, 李登, 巫世晶. 磨料水射流铣削钛合金深度与表面粗糙度研究[J]. 机械科学与技术, 2023, 42(7): 1063-1069. doi: 10.13433/j.cnki.1003-8728.20220045
引用本文: 杜航, 熊杰, 陈炜, 李登, 巫世晶. 磨料水射流铣削钛合金深度与表面粗糙度研究[J]. 机械科学与技术, 2023, 42(7): 1063-1069. doi: 10.13433/j.cnki.1003-8728.20220045
DU Hang, XIONG Jie, CHEN Wei, LI Deng, WU Shijing. Investigation of Depth and Surface Roughness in Abrasive Water Jet Milling of Titanium Alloy[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(7): 1063-1069. doi: 10.13433/j.cnki.1003-8728.20220045
Citation: DU Hang, XIONG Jie, CHEN Wei, LI Deng, WU Shijing. Investigation of Depth and Surface Roughness in Abrasive Water Jet Milling of Titanium Alloy[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(7): 1063-1069. doi: 10.13433/j.cnki.1003-8728.20220045

磨料水射流铣削钛合金深度与表面粗糙度研究

doi: 10.13433/j.cnki.1003-8728.20220045
基金项目: 

国家自然科学基金项目 51805188

详细信息
    作者简介:

    杜航(1996-), 硕士研究生, 研究方向为水射流理论与新技术, dh0802@whu.edu.cn

    通讯作者:

    巫世晶, 教授, 博士生导师, wsj@whu.edu.cn

  • 中图分类号: V261.91

Investigation of Depth and Surface Roughness in Abrasive Water Jet Milling of Titanium Alloy

  • 摘要: 磨料水射流铣削技术柔性大、工艺参数复杂,其加工性能难以有效控制。针对这一问题,本文首先通过响应曲面法研究了磨料水射流铣削钛合金时典型工艺参数对铣削深度和表面粗糙度的影响,并采用传统回归方式建立了经验预测模型;其次在结合磨粒磨损理论、高斯轮廓模型和表面成形分析的基础上,进一步建立了铣削深度和表面粗糙度的半经验预测模型;然后利用实验数据进行了参数标定;最后通过实验验证和对比了两种模型。结果表明,两种预测模型的平均误差均小于15%,相比经验模型,半经验模型既可以解释参数影响和铣削机理,又可以保证预测的准确率和稳定性,对于控制铣削深度和表面质量具有重要价值。
  • 图  1  磨料水射流Zig-Zag铣削路径

    Figure  1.  Zig-Zag milling path with abrasive water jet

    图  2  横向进给对磨痕廓形的影响

    Figure  2.  The influence of transverse feeding on the profile shape of the grinding mark

    图  3  不同经验模型的残差正态概率分布

    Figure  3.  Normal probability distributions of the residuals for different empirical models

    图  4  不同经验模型的残差与预测响应间的关系

    Figure  4.  The relationship between residuals and predicted responses for different empirical models

    图  5  单个工艺参数对不同铣削特性响应的影响

    Figure  5.  The influence of individual process parameters on the responses of different milling characteristics

    图  6  单道磨痕的几何轮廓解析

    Figure  6.  Geometrical profile analysis of a single grinding mark

    图  7  不同方向的一维轮廓粗糙度形成原理

    Figure  7.  Formation principle of one-dimensional profile roughness in different directions

    图  8  铣削性能实验值与预测模型值的对比

    Figure  8.  Comparison of experimental values and predicted model values for milling performance

    表  1  工件和磨料的材料性能

    Table  1.   Material properties of the workpiece and abrasive

    材料 σf/MPa σb/MPa ρ/(g·cm-3) 莫氏硬度 粒度/目
    Ti6Al4V ≥860 ≥895 4.51 - -
    石榴石 - - 3.4~4.3 7.5 80
    下载: 导出CSV

    表  2  响应曲面实验设计中的因素及水平

    Table  2.   Factors and levels in the response surface experimental design

    工艺因素 水平1 水平2 水平3
    -1 0 1
    A: 射流压力P/MPa 220 260 300
    B: 磨料流量ma/(g·min-1) 200 480 760
    C: 靶距D/mm 6 14 22
    D: 射流角度α/(°) 30 60 90
    E: 进给速度u/(mm·s-1) 30 50 70
    F: 横向进给量S/mm 0.4 0.6 0.8
    下载: 导出CSV

    表  3  验证实验中的因素及水平

    Table  3.   Factors and levels in validation experiments

    工艺因素 水平 水平 水平 水平 水平 水平
    射流压力P -1, 0, 1 0 0 0 0 0
    磨料流量 0 -1, 0, 1 0 0 0 0
    靶距D 0 3 -1, 0, 1 0 0 0
    射流角度α 1 1 1 -1, 0, 1 1 1
    进给速度u 0 0 0 0 -1, 0, 1 0
    横向进给量S -1 -1 -1 -1 -1 -1, 0, 1
    下载: 导出CSV

    表  4  模型误差统计

    Table  4.   Model error statistics %

    铣削特性 模型类型 平均误差 最大误差 误差标准差
    铣削深度 经验模型 9.57 34.92 7.71
    半经验模型 9.38 24.57 6.62
    X向粗糙度 经验模型 10.87 31.16 8.75
    半经验模型 13.81 28.41 7.75
    Y向粗糙度 经验模型 14.58 36.81 9.12
    半经验模型 13.54 30.53 10.09
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-21
  • 刊出日期:  2023-07-25

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