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硬磁软连续体的磁畴可重编程与优化设计

赵勇 李木军

赵勇, 李木军. 硬磁软连续体的磁畴可重编程与优化设计[J]. 复合材料学报, 2024, 42(0): 1-12.
引用本文: 赵勇, 李木军. 硬磁软连续体的磁畴可重编程与优化设计[J]. 复合材料学报, 2024, 42(0): 1-12.
ZHAO Yong, LI Mujun. Reprogrammable magnetic domains and optimized design of a hard magnetic soft continuum[J]. Acta Materiae Compositae Sinica.
Citation: ZHAO Yong, LI Mujun. Reprogrammable magnetic domains and optimized design of a hard magnetic soft continuum[J]. Acta Materiae Compositae Sinica.

硬磁软连续体的磁畴可重编程与优化设计

基金项目: 统筹推进世界一流大学和一流学科建设专项资金(YD2090003002);国家自然科学基金(51475442)
详细信息
    通讯作者:

    李木军,博士,副教授,硕士生导师,研究方向为超精密加工、光学制造、3D打印技术、磁性机器人 E-mail: lmn@ustc.edu.cn

  • 中图分类号: TB33

Reprogrammable magnetic domains and optimized design of a hard magnetic soft continuum

Funds: USTC Research Funds of the Double First-Class Initiative (YD2090003002); National Natural Science Foundation of China (51475442)
  • 摘要: 硬磁软材料是一种新型的磁驱动复合材料,其中硬磁软连续体具有响应快、变形大、生物安全性好的特点,在生物医疗,仿生,软体触手等领域获得了广泛的关注。然而,现有的硬磁软连续体通过脉冲磁场磁化,磁畴模式单一,难以满足复杂的变形需求,此外如何解决硬磁软连续体在复杂变形下的磁畴优化设计问题也是当前的重要挑战。针对该问题,本文开发了基于二氧化铬的硬磁软连续体,通过激光直写实现了复杂磁畴模式的可重构编程。而后针对不同变形需求的磁畴分段设计问题,对尺寸大小、磁粉含量和磁畴方向进行优化设计。最后针对多种变形需求进行磁畴的优化设计并通过实验进行验证,同时进行功能化展示。

     

  • 图  1  硬磁软连续体的居里温度

    Figure  1.  Curie temperature of the hard magnetic soft continuum

    图  2  硬磁软连续体激光加热重复编程原理图

    Figure  2.  Schematic diagram of repetitive programming of laser heating of hard magnetic soft continuum

    图  3  激光加热下样品表面的温度变化

    Figure  3.  Temperature variation of sample surface under laser heating

    图  4  硬磁软材料变形原理图

    Figure  4.  Principle diagram of deformation of hard magnetic soft material

    图  5  硬磁软连续体的SEM表征

    Figure  5.  SEM characterization of a hard magnetic soft continuum

    图  6  不同尺寸大小的硬磁软连续体磁学性能

    Figure  6.  Magnetic properties of hard magnetic soft continuum with different size dimensions

    图  7  不同CrO2体积分数硬磁软连续体的力学性能

    Figure  7.  Mechanical properties of hard magnetic soft continuum with different CrO2 volume fractions

    图  8  不同CrO2体积分数硬磁软连续体的磁学性能

    Figure  8.  Magnetic properties of hard magnetic soft continuum with different CrO2 volume fractions

    图  9  CrO2体积分数的硬磁软连续体的仿真变形图

    Figure  9.  Simulated deformation of hard magnetic soft continuum with CrO2 volume fraction

    图  10  随机优化算法

    Figure  10.  Randomized optimization algorithm

    图  11  邻域优化算法

    Figure  11.  Neighborhood optimization algorithm

    图  12  悬链线函数型曲线结果

    Figure  12.  Results of functional type curves of hanging chain lines

    图  13  sin函数型曲线结果

    Figure  13.  Results of sin function type curves

    图  14  sinc函数型曲线结果

    Figure  14.  Results of sinc function type curves

    图  15  阻尼函数型曲线结果

    Figure  15.  Damping function type curve results

    图  16  悬链线函数型曲线

    Figure  16.  Functional type curve of hanging chain line

    图  17  sin函数型曲线

    Figure  17.  sin function type curve

    图  18  sinc函数型曲线

    Figure  18.  sinc function type curve

    图  19  阻尼函数型曲线

    Figure  19.  Damping function type curve

    图  20  抓取的异形物

    Figure  20.  Gripped foreign objects

    图  21  抓取对称结构异形物的变形

    Figure  21.  Deformation of grasping objects with symmetrical structures

    图  22  抓取非对称结构异形物的变形

    Figure  22.  Deformation of grasping objects with asymmetrical structures

    图  23  抓取对称结构异形物的对比

    Figure  23.  Contrast of grasping objects with symmetrical structures

    图  24  抓取非对称结构异形物的对比

    Figure  24.  Contrast of grasping objects with asymmetrical structures

    表  1  磁畴优化算法

    Table  1.   Magnetic Domain Optimization Algorithm

    A Random optimization algorithm combining neighborhood optimization with adaptive step size:
    Input: Target curve
    Output: Optimal magnetic domain direction parameters for fitting the corresponding target curve of hard magnetic soft continuum
    1: Divide the target curve according to the length of the finite element mesh, so that the target curve nodes correspond one-to-one with the corresponding finite element nodes;
    2: Set a marker for the number of repeated iterations, Rt=0;
    3: Randomly initialize the direction of magnetic domains $ \left({\alpha }_{1},{\alpha }_{2},\dots {\alpha }_{n}\right) $ Generate 30 samples;
    4: if Rt < 3 do
    5: Perform finite element simulation;
    6: Calculate the average node error D between each deformed sample and the theoretical curve;
    Obtain the contemporary optimal solution $ {D}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{t}} $, and compare the sizes of $ {D}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{t}-1} $ and $ {D}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{t}} $;
    7: if $ {D}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{t}} < {D}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{t}-1} $ do
    8: Use random optimization algorithm, Rt =0;
    Update iterative magnetic domain step size $ \beta =4\mathrm{*}{D}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{t}} $;
    Update the current optimal magnetic domain direction parameter group $ \left({\alpha }_{1},{\alpha }_{2},\dots {\alpha }_{\mathrm{n}}\right) $;
    Update magnetic domain direction parameters, $ {\alpha }_{\mathrm{i}}^{\mathrm{t}+1}={\alpha }_{{\mathrm{o}\mathrm{p}\mathrm{t}}_{\mathrm{i}}}+\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{o}\mathrm{m}\left(-\beta ,0,\beta \right) $;
    Generate 30 new samples;
    end
    9: if $ {D}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{t}-1} < {D}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{t}} $ do
    10: Use neighborhood optimization algorithm, Rt = Rt +1;
    Update iterative magnetic domain step size $ \beta =4\mathrm{*}{D}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{t}-1} $;
    Calculate the average node error $ ({d}_{1},{d}_{2},\dots ,{d}_{\mathrm{n}}) $ for each segment of the sample corresponding to$ {D}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{t}-1} $;
    Sort the error segments in descending order, and select the segment j in the Rt position;
    Update magnetic domain direction parameters,$ {\alpha }_{\mathrm{i}}^{\mathrm{t}+1}={\alpha }_{{\mathrm{o}\mathrm{p}\mathrm{t}}_{\mathrm{i}}}+\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{g}\mathrm{e}\left(-\beta ,0,\beta \right) $,$ i=j-1,j,j+1 $; Generate new samples;
    end
    11: end
    12:Output the current optimal magnetic domain direction parameter set $ \left({\alpha }_{{\mathrm{o}\mathrm{p}\mathrm{t}}_{1}},{\alpha }_{{\mathrm{o}\mathrm{p}\mathrm{t}}_{2}},\dots {\alpha }_{{\mathrm{o}\mathrm{p}\mathrm{t}}_{\mathrm{n}}}\right) $
    下载: 导出CSV

    表  2  不同函数构型的分段磁化方向和分段长度

    Table  2.   Segmented magnetization directions and segment lengths for different functional configurations

    Catenary function type Magnetic domain direction/(°) 158 168 183 185 2 -16 28 6
    fractional length/mm 6.3 6.3 6.3 6.4 6.4 6.3 6.3 6.3
    sin function type Magnetic domain direction/(°) 31 9 173 147 154 170 -22 45
    fractional length/mm 6.7 6.7 6.8 6.8 6.8 6.8 6.7 6.7
    sinc function type Magnetic domain direction/(°) 172 30 9 30 6 170 164 141
    fractional length /mm 8
    6.8 6.8 7 7 7 7 6.9
    Damping function type Magnetic domain direction/(°) 7 -20 177 186 163 119 51 53
    fractional length/mm 6.7 6.8 5.6 5.6 5.6 5.7 5.2 5.2
    下载: 导出CSV

    表  3  对称结构物体左侧的分段磁化方向和分段长度

    Table  3.   Direction of segment magnetization and segment length on the left side of a symmetrically structured object

    Magnetic domain direction/(°) −27 −6 13 70 17 −37 −39 −17 22 15 54 54 100
    Fractional length/mm 4.2 4.2 4.2 4.3 4.3 4.5 4.5 4.6 4.1 4.1 4.1 4.1 4.1
    下载: 导出CSV

    表  4  非对称结构物体右侧的分段磁化方向和分段长度

    Table  4.   Segmented magnetization directions and segment lengths on the right side of an asymmetrically structured object

    Magnetic domain direction/(°) −4 51 45 −63 −25 0 21 −12 −22 55 48 −70
    Fractional length/mm 4.7 4.7 4.8 4.7 4.7 4.8 5.1 5.1 5.1 5.1 5.1 5.1
    下载: 导出CSV
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  • 收稿日期:  2024-02-06
  • 修回日期:  2024-04-01
  • 录用日期:  2024-04-15
  • 网络出版日期:  2024-05-16

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