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磨料流加工均匀性仿真物理模型的选用与简化方法

史苏芳 张保财 王夏宇 王新昶

史苏芳, 张保财, 王夏宇, 王新昶. 磨料流加工均匀性仿真物理模型的选用与简化方法[J]. 金刚石与磨料磨具工程, 2024, 44(5): 652-664. doi: 10.13394/j.cnki.jgszz.2023.0267
引用本文: 史苏芳, 张保财, 王夏宇, 王新昶. 磨料流加工均匀性仿真物理模型的选用与简化方法[J]. 金刚石与磨料磨具工程, 2024, 44(5): 652-664. doi: 10.13394/j.cnki.jgszz.2023.0267
SHI Sufang, ZHANG Baocai, WANG Xiayu, WANG Xinchang. The selection and simplification of physical models for simulation of abrasive flow machining uniformity[J]. Diamond & Abrasives Engineering, 2024, 44(5): 652-664. doi: 10.13394/j.cnki.jgszz.2023.0267
Citation: SHI Sufang, ZHANG Baocai, WANG Xiayu, WANG Xinchang. The selection and simplification of physical models for simulation of abrasive flow machining uniformity[J]. Diamond & Abrasives Engineering, 2024, 44(5): 652-664. doi: 10.13394/j.cnki.jgszz.2023.0267

磨料流加工均匀性仿真物理模型的选用与简化方法

doi: 10.13394/j.cnki.jgszz.2023.0267
基金项目: 上海市自然科学基金(22ZR1433200);国家自然科学基金面上项目(52175423)。
详细信息
    通讯作者:

    王新昶,男,1988年生,博士,副教授,博士研究生导师。主要研究方向:磨料流加工技术,金刚石材料合成、后加工及应用技术。 E-mail:wangxinchang@sjtu.edu.cn

  • 中图分类号: TG58

The selection and simplification of physical models for simulation of abrasive flow machining uniformity

  • 摘要: 新能源汽车齿轮磨齿加工后的精密抛光对于改善其噪声、振动和声振粗糙度性能至关重要,磨料流加工是最适用于复杂齿面高效抛光的关键技术之一,而夹具设计是实现工艺目标(减小波纹度及粗糙度,同时尽量不破坏齿面精度)的关键。结合仿真方法设计磨料流加工夹具优势突出,但物理模型的选择与仿真结果的准确性和计算成本之间存在着矛盾。采用不同黏度介质、黏度模型和流动模型进行仿真试验,分析体现加工均匀性的流体压力分布、速度矢量、壁面剪切力和流线分布云图,探究狭缝模型中磨料流稳态仿真结果,发现不同物理模型的仿真结果具有相似的分布趋势,能够实现加工区域流线的一致,证明了用简化的物理模型替代复杂的物理模型进行夹具优化仿真的可行性,而复杂模型有望用于深入分析磨料流体流动行为及材料去除机理。基于简化模型,采用最简单的牛顿流体──水作为介质,以加工区域流线分布均匀化作为优化目标,进行齿轮轴磨料流夹具设计优化,成功实现了去除齿轮鬼阶的目标。

     

  • 图  1  双向磨料流加工原理示意图

    Figure  1.  Schema of two-side AFM process

    图  2  流道几何模型及网格划分

    Figure  2.  Geometry model and grid division of flow field

    图  3  不同黏度牛顿流体层流仿真结果

    Figure  3.  Newtonian fluids simulation results with different viscosities

    图  4  低黏介质不同非牛顿流体黏度模型层流仿真结果

    Figure  4.  Laminar flow simulation results of different non-Newtonian fluid viscosity models in low viscosity media

    图  5  高黏介质不同非牛顿流体黏度模型层流仿真结果

    Figure  5.  Laminar flow simulation results of different non-Newtonian fluid viscosity models in high viscosity media

    图  6  低黏介质牛顿流体湍流仿真结果

    Figure  6.  Low viscosity media simulation results of Newtonian fluid with different turbulence models

    图  7  高黏介质牛顿流体湍流仿真结果

    Figure  7.  High viscosity media simulation results of Newtonian fluid with different turbulence models

    图  8  低黏介质非牛顿流体Carreau模型湍流仿真结果

    Figure  8.  Low viscosity media simulation results of non-Newtonian Carreau model with different turbulence models

    图  9  高黏介质非牛顿流体Power-Law模型湍流仿真结果

    Figure  9.  High viscosity media simulation results of non-Newtonian Power-Law model with different turbulence models

    图  10  齿轮轴及夹具设计

    Figure  10.  Gear Shaft and Fixture Design

    图  11  齿轮夹具设计仿真流线分布

    Figure  11.  Wall shear streamline of gear shaft fixture design

    图  12  齿轮波纹分析报告

    Figure  12.  Ripple analysis reports

    表  1  因素水平表

    Table  1.   Factor level table

    试验因素 各因素试验条件(水平)
    黏度介质 V V1=水
    V2=低黏
    V3=高黏
    黏度模型 N N1=Power-Law
    N2=Carreau
    N3=Cross
    流动模型 T 层流模型 T0=Laminar
    湍流模型 T1=Spalart-Allmaras
    T2=Standard k-ε
    T3=SST k-omega
    T4=Reynolds Stress
    T5=LES
    下载: 导出CSV

    表  2  流体介质参数

    Table  2.   Fluid medium parameters

    流体介质 密度 ρ / (kg·m−3) 黏度 η / (Pa·s) 雷诺数 Re 湍流强度 I / %
    牛顿流体参数 998.2 0.001 286 673.597 3.326
    低黏 2 865.5 1.33 232.201 5 8.098
    高黏 1308.16 412.11 0.001 165 7 37.220
    非牛顿流体
    黏度模型参数
    K / (Pa·sn) n η0 / (Pa·s) η / (Pa·s) λ / s 最小黏度 / (kg·m−1s−1) 最大黏度 / (kg·m−1s−1)
    Power-Law 低黏 33.29 0.488
    高黏 7295.23 0.358 0 196 438.41
    Carreau 低黏 0.36 347.3 0 60
    高黏 0.41 196438.41 0 138.04
    Cross 低黏 0.221 514.63 50.91
    高黏 1.813 236934.77 70.85
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-12-06
  • 修回日期:  2024-01-02
  • 录用日期:  2024-01-16
  • 网络出版日期:  2024-01-16
  • 刊出日期:  2024-10-01

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