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Multi-objective parameter optimization of abrasive water jet polishing for fused silica

LI Qian YAO Peng DENG Hong-xing FENG Chen-yu XU Chong-hai QU Shuo-shuo YANG Yu-ying ZHU Hong-tao HUANG Chuan-zhen

李倩, 姚鹏, 邓红星, 冯辰宇, 许崇海, 屈硕硕, 杨玉莹, 朱洪涛, 黄传真. 磨料水射流抛光熔石英玻璃的多目标参数优化[J]. 中国光学(中英文). doi: 10.37188/CO.EN-2025-0006
引用本文: 李倩, 姚鹏, 邓红星, 冯辰宇, 许崇海, 屈硕硕, 杨玉莹, 朱洪涛, 黄传真. 磨料水射流抛光熔石英玻璃的多目标参数优化[J]. 中国光学(中英文). doi: 10.37188/CO.EN-2025-0006
LI Qian, YAO Peng, DENG Hong-xing, FENG Chen-yu, XU Chong-hai, QU Shuo-shuo, YANG Yu-ying, ZHU Hong-tao, HUANG Chuan-zhen. Multi-objective parameter optimization of abrasive water jet polishing for fused silica[J]. Chinese Optics. doi: 10.37188/CO.EN-2025-0006
Citation: LI Qian, YAO Peng, DENG Hong-xing, FENG Chen-yu, XU Chong-hai, QU Shuo-shuo, YANG Yu-ying, ZHU Hong-tao, HUANG Chuan-zhen. Multi-objective parameter optimization of abrasive water jet polishing for fused silica[J]. Chinese Optics. doi: 10.37188/CO.EN-2025-0006

磨料水射流抛光熔石英玻璃的多目标参数优化

Multi-objective parameter optimization of abrasive water jet polishing for fused silica

doi: 10.37188/CO.EN-2025-0006
Funds: Supported by National Key Research and Development Program of China (No. 2023YFC2413301); National Natural Science Foundation of China (No. U23A20632); Major Basic Research of Shandong Provincial Natural Science Foundation (No. ZR2023ZD34)
More Information
    Author Bio:

    LI Qian (1993—), female, born in Jinan, Shandong Province. She obtained her bachelor's degree from Dezhou University in 2017. Currently, she is a postgraduate jointly cultivated by the School of Mechanical Engineering of Qilu University of Technology and the School of Mechanical Engineering of Shandong University. Her main research directions are ultra-precision machining and abrasive water jet polishing technology. E-mail: qluliqian@163.com

    YAO Peng (1979—), male, born in Dalian, Liaoning Province, PhD, Professor, Doctoral Supervisor. He received his doctoral degree from Northeastern University (Japan) in 2011, Mainly engaged in research on grinding and ultra precision machining technology, multi energy field composite precision machining technology, and laser micro/nano machining technology. E-mail: yaopeng@sdu.edu.cn

    Corresponding author: yaopeng@sdu.edu.cn
  • 摘要:

    磨料水射流抛光技术作为一种非接触式超精密加工方法,凭借其稳定的材料去除函数、无亚表面损伤特性及强形状适应性,在光学元件加工领域具有重要应用价值。本研究通过计算流体动力学(CFD)数值模拟方法,系统分析了射流压力、喷嘴直径及入射角度对抛光流场压力分布、速度分布及壁面剪切力分布的作用规律。基于Box-Behnken实验设计,构建了响应面回归模型,系统研究了工艺参数对熔石英玻璃的材料去除率(MRR)和表面粗糙度(Ra)的影响机制。实验结果表明:通过增大射流压力和喷嘴直径可显著提高MRR,该规律与流场仿真揭示的剪切应力分布特征一致;但增大射流压力和入射角度会导致Ra增大,不利于表面质量提升。通过遗传算法(GA)多目标优化建立Pareto解集,成功实现了加工效率与表面质量的协同优化,在射流压力2 MPa、喷嘴直径0.3mm和入射角度30°的参数组合下MRR达169.05 μm³/s、Ra低至0.50 nm;实验验证表明,模型预测值与实测值误差仅为4.4%(MRR)和3.8%(Ra),验证了模型的可靠性。本研究建立的参数优化体系为复杂曲面光学元件的超精密抛光提供了理论依据与技术支持。

     

  • Figure 1.  Schematic diagram of AWJP

    Figure 2.  Experimental setup

    Figure 3.  The SEM image of CeO2 abrasive

    Figure 4.  The white light 3D interferometer image of (a) the removal profile and (b) cross-sectional TIF

    Figure 5.  Polished surface topography via white light interferometry

    Figure 6.  Velocity cloud plots at different impinging angles (a) 30°; (b) 45° and (c) 60°

    Figure 7.  Simulation curves for different impinging angles (a) velocity curve; (b) static pressure curve and (c) wall shear stress curve

    Figure 8.  Simulation curves for different jet pressure (a) velocity curve; (b) static pressure curve and (c) wall shear stress curve

    Figure 9.  Simulation curves for different nozzle diameters (a) velocity curve; (b) static pressure curve and (c) wall shear stress curve

    Figure 10.  Normal plots of residuals for (a) MRR and (b) Ra

    Figure 11.  Perturbation plots obtained from RSM analysis showing the influence of individual process variables on (a) MRR and (b) Ra

    Figure 12.  The material removal process under different jet impinging (a) Small impinging angle and (b) Large impinging angle

    Figure 13.  3D response surface plots depicting the interactions between independent variables and their effect on MRR (a-c) and Ra (d-f)

    Figure 14.  Pareto footprint diagram of the objective function

    Table  1.   AWJP parameters

    AWJP parameters levels
    −1 0 1
    Jet pressure (MPa) 2 3 4
    Nozzle diameter (mm) 0.3 0.5 0.7
    Impinging angle (°) 30 45 60
    下载: 导出CSV

    Table  2.   The results of the response surface regression experiments

    AWJP parameters Output responses
    Run jet pressure (MPa) nozzle diameter (mm) impinging angle (°) MRR (μm3/s) Ra (nm)
    1 2 0.5 30 262.75 0.56
    2 4 0.3 45 907.42 1.12
    3 3 0.5 45 735.50 0.81
    4 3 0.5 45 846.42 0.87
    5 2 0.3 45 221.42 0.55
    6 3 0.5 45 766.75 0.86
    7 3 0.5 45 821.33 0.80
    8 4 0.7 45 1873.25 1.22
    9 2 0.5 60 208.67 0.71
    10 4 0.5 30 1391.92 0.94
    11 3 0.3 60 439.33 0.91
    12 3 0.3 30 613.08 0.72
    13 4 0.5 60 1011.42 1.35
    14 3 0.5 45 831.25 0.92
    15 2 0.7 45 470.08 0.63
    16 3 0.7 60 961.42 0.93
    17 3 0.7 30 1304.92 0.78
    下载: 导出CSV

    Table  3.   Analysis of variance for MRR

    Source Sum of squares Degree of Freedom Mean square F-value P-value Significance
    Model 3.082$ e $+06 9 3.425$ e $+05 187.33 < 0.0001 significant
    A-jet pressure 2.021$ e $+06 1 2.021$ e $+06 1105.56 < 0.0001
    B-nozzle diameter 7.372$ e $+05 1 7.372$ e $+05 403.22 < 0.0001
    C-impinging angle 1.132$ e $+05 1 1.132$ e $+05 61.95 0.0001
    AB 1.286$ e $+05 1 1.286$ e $+05 70.33 < 0.0001
    AC 26637.50 1 26637.50 14.57 0.0066
    BC 7203.77 1 7203.77 3.94 0.0875
    A2 1964.92 1 1964.92 1.07 0.3343
    B2 33648.28 1 33648.28 18.41 0.0036
    C2 15136.43 1 15136.43 8.28 0.0237
    Residual 12797.12 7 1828.16
    Lack of Fit 3945.28 3 1315.09 0.5943 0.6512 not significant
    Pure Error 8851.85 4 2212.96
    Cor Total 3.095$ \mathrm{e} $$ e $+06 16
    下载: 导出CSV

    Table  5.   Fit statistics of ANOVA

    Std.Dev. Mean C.V.% R2 Adjusted R2 Predicted R2 Adeq Precision
    MRR 42.76 803.94 5.32 0.9959 0.9905 0.9751 51.073
    Ra 0.0524 0.8635 6.07 0.9743 0.9413 0.7717 19.1547
    下载: 导出CSV

    Table  4.   Analysis of variance for Ra

    Source Sum of squares Degree of Freedom Mean square F-value P-value Significance
    Model 0.7290 9 0.0810 29.48 < 0.0001 significant
    A-jet pressure 0.5940 1 0.5940 216.24 < 0.0001
    B-nozzle diameter 0.0084 1 0.0084 3.08 0.1229
    C-impinging angle 0.1012 1 0.1012 36.86 0.0005
    AB 0.0001 1 0.0001 0.0364 0.8541
    AC 0.0169 1 0.0169 6.15 0.0422
    BC 0.0004 1 0.0004 0.1456 0.7141
    A2 0.0073 1 0.0073 2.64 0.1483
    B2 0.0008 1 0.0008 0.2793 0.6135
    C2 0.0001 1 0.0001 0.0188 0.8949
    Residual 0.0192 7 0.0027
    Lack of Fit 0.0098 3 0.0033 1.37 0.3720 not significant
    Pure Error 0.0095 4 0.0024
    Cor Total 0.7482 16
    下载: 导出CSV

    Table  6.   Partial Pareto solution set for multi-objective optimization

    run A B C $ f(MRR) $ $ f(Ra) $
    1 2.00 0.3 30 176.86 0.52
    2 2.10 0.3 30 218.77 0.53
    3 3.90 0.7 30 1955.06 0.99
    4 2.32 0.7 30 743.00 0.64
    5 2.00 0.6 30 390.57 0.59
    6 2.68 0.7 30 1045.99 0.70
    7 3.93 0.7 30 1982.46 1.00
    8 2.10 0.7 30 512.08 0.60
    9 4.00 0.7 30 2036.53 1.02
    10 3.25 0.7 30 1473.19 0.82
    11 2.96 0.7 30 1253.51 0.76
    12 3.20 0.7 30 1433.27 0.81
    ··· ··· ··· ···
    下载: 导出CSV

    Table  7.   Comparative analysis of experiments

    Run Indicator Pareto solutions Experimental value Error
    1 MRR
    (μm3/s)
    176.86 169.05 4.4%
    8 512.08 507.65 0.9%
    11 1253.51 1247.25 0.5%
    1 Ra(nm) 0.52 0.50 3.8%
    8 0.60 0.58 3.3%
    11 0.76 0.78 2.6%
    下载: 导出CSV
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  • 收稿日期:  2025-02-08
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