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基于RSM和SVR-IDBO的柔性压力传感单元灵敏度性能预测与优化

唐颖 陈垲 何宋西莹 严少秋 雷建勇 林远长 何国田

唐颖, 陈垲, 何宋西莹, 等. 基于RSM和SVR-IDBO的柔性压力传感单元灵敏度性能预测与优化[J]. 复合材料学报, 2024, 42(0): 1-15.
引用本文: 唐颖, 陈垲, 何宋西莹, 等. 基于RSM和SVR-IDBO的柔性压力传感单元灵敏度性能预测与优化[J]. 复合材料学报, 2024, 42(0): 1-15.
TANG Ying, CHEN Kai, HE Songxiying, et al. Sensitivity performance prediction and optimisation of flexible pressure sensing unit based on RSM and SVR-IDBO[J]. Acta Materiae Compositae Sinica.
Citation: TANG Ying, CHEN Kai, HE Songxiying, et al. Sensitivity performance prediction and optimisation of flexible pressure sensing unit based on RSM and SVR-IDBO[J]. Acta Materiae Compositae Sinica.

基于RSM和SVR-IDBO的柔性压力传感单元灵敏度性能预测与优化

基金项目: 国家重点研发计划(2018 YFB1306601);重庆市在渝高校与中科院所属院所合作项目(HZ2021011);重庆市技术创新与应用发展专项(cstc2021 jscx-cylhX0009)
详细信息
    通讯作者:

    何国田,博士,二级教授、正高级工程师,博士生导师,研究方向为智能机器人、触觉传感器、复合材料 E-mail: heguotian@cigit.ac.cn

  • 中图分类号: TP3-05

Sensitivity performance prediction and optimisation of flexible pressure sensing unit based on RSM and SVR-IDBO

Funds: National Key R&D Program (2018 YFB1306601); Chongqing Municipal Cooperation Project between Chongqing Universities and Institutes Affiliated to the Chinese Academy of Sciences (HZ2021011); Chongqing Municipal Special Project for Technological Innovation and Application Development (cstc2021 jscx-cylhX0009)
  • 摘要: 为优化柔性压力传感单元制备工艺,提升传感器灵敏度特性。本研究通过建立灵敏度解析模型,确定了影响其性能表现的主要因素,采用机械共混的方式,通过调节碳纳米管(CNT)、多层石墨烯(MLG)、搅拌时间(Mixing time)、成型温度(Molding temperature)等参数优化了柔性传感单元的灵敏度性能。首先在单因素分析的基础上,应用实验设计(DOE)中的中心复合实验方法(CCD)进行多因素实验设计,通过响应面法(RSM)和支持向量机(SVR)对多因素的交互影响进行了分析,并分别建立了灵敏度预测模型。其次根据决定系数($ {R}^{2} $)、均方根误差(Rmse)和平均误差率(Mae)对两种模型进行评估与定型,模型性能对比结果表明,通过超参优化后的SVR模型表现出更高水平的准确性和可预测性。然后基于改进的蜣螂优化算法(IDBO)对模型进行迭代优化,得到了比早期实验更好的灵敏度性能。仿真结果显示在0~30kPa的单轴压力下,当CNTs含量为2.3wt%、MLG含量为1.9wt%、混合时间15 min、成型温度78℃时,灵敏度达到0.5512 kPa−1,经过实验验证,与实际灵敏度(0.5371 kPa−1)的相对误差为2.625%,且与同类型研究相比较,本研究的传感单元灵敏度性能也处较高水平。证明该方法有助于寻找最佳的传感器含量配比与制备工艺,提升实验效率,节约实验成本,为快速制备高性能电容式柔性压力传感单元提供了新思路。

     

  • 图  1  触觉敏感单元制备流程图

    Figure  1.  Flow Chart for Preparation of Pressure Sensor

    图  2  柔性触觉敏感单元样品

    Figure  2.  Samples of Flexible Tactile Sensitive Units

    图  3  触觉敏感单元改性前后对比

    Figure  3.  Comparison between before and after modification of the tactile sensitivity unit

    图  4  压力传感器性能测试系统

    Figure  4.  pressure sensor performance test system

    图  5  渗滤复合材料作为介电层的柔性电容式压力传感器的工作原理图

    Figure  5.  Working principle diagram of flexible capacitive pressure sensor with percolation composite as dielectric layer

    图  6  输入参数的单因素影响:(a) CNTs单因素影响;(b) MLGs单因素影响;(c) 搅拌时间单因素影响;(d) 成型温度单因素影响

    Figure  6.  Single factor effect of input parameter:(a) single-factor effect of CNTs; (b) single-factor effect of MLGs; (c) single-factor effect of mixing time; (d) single-factor effect of molding temperature

    图  7  SVR模型示意图

    Figure  7.  SVR model diagram

    图  8  K折交叉验证示意图

    Figure  8.  K sketch map of cross validation

    图  9  蜣螂优化算法流程图

    Figure  9.  Dung beetle optimization algorithm flowchart

    图  10  改进蜣螂优化算法流程图

    Figure  10.  Improved dung beetle optimization algorithmflowchart

    图  11  算法性能对比收敛曲线图

    Figure  11.  Algorithm Performance Comparison Convergence Plot

    图  12  灵敏度三维响应面图:(a)碳纳米管(CNT)/石墨烯(MLG)相互作用;(b)CNT/搅拌时间相互作用;(c)CNT/成型温度相互作用;(d)MLG/搅拌时间相互作用;(e)MLG/成型温度相互作用;(f)搅拌/成型温度相互作用

    Figure  12.  Three-dimensional response surface plots of sensitivity: (a) Interaction between CNT/MLG content; (b) Interaction between CNT/mixing time; (c) Interaction between CNT/molding temperature; (d) Interaction between MLG/mixing time; (e) Interaction between MLG/molding temperature; (f) Interaction between mixing time/molding temperature

    图  13  SVR模型最佳参数优化结果

    Figure  13.  Optimal parameter optimization results of SVR model

    图  14  SVR和RSM模型的5折交叉验证的雷达图

    Figure  14.  Radar diagrams of SVR and RSM models with 5 fold cross validation

    图  15  改进蜣螂优化算法(IDBO)收敛过程

    Figure  15.  Dung beetle optimization algorithm (IDBO) convergence process

    图  16  灵敏度测试电容输出响应曲线

    Figure  16.  Sensitivity test capacitor output response curve

    图  17  触觉单元相关性能测试图

    Figure  17.  Haptic unit related performance graphs

    表  1  单因素实验方案

    Table  1.   Single-factor protocol

    IndexContent of CNTs/gContent of MLGs/gMixing time/minMolding temperature/℃Analysis of factors affecting
    10.4,0.5,0.6,0.7,0.8,0.9,1.0,1.10.61580CNT
    20.70.4,0.5,0.6,0.7,0.8,0.9,1.0,1.11580MLG
    30.70.610,15,20,25,3080Mixing time
    40.70.61540,60,80,100,120Molding temperature
    下载: 导出CSV

    表  2  四个因素的水平和编码

    Table  2.   Level and code of four factors

    Code Levels Factor
    CNTs/g MLGs/g Mixing time/min Molding temperature/℃
    −2 0.7 0.6 10 40
    −1 0.8 0.7 15 60
    0 0.9 0.8 20 80
    1 1.0 0.9 25 100
    2 1.1 1.0 30 120
    下载: 导出CSV

    表  3  多因素中心组合实验设计(CCD)与结果

    Table  3.   Multifactorial central combinatorial experimental design (CCD) and results

    Run Factors Sensitivity
    CNT MLG Mixing time Molding temperature
    1 1 −1 −1 1 0.227
    2 0 0 2 0 0.13
    3* 0 0 0 0 0.118
    4 −1 −1 −1 1 0.058
    5 1 1 1 1 0.191
    6 1 −1 −1 −1 0.208
    7 −1 1 1 1 0.025
    8* 0 0 0 0 0.145
    9 −1 1 −1 1 0.05
    10 −1 −1 1 −1 0.125
    11 −2 0 0 0 0.05
    12 0 −2 0 0 0.185
    13 0 0 −2 0 0.193
    14 −1 1 1 −1 0.164
    15 1 1 −1 −1 0.375
    16 1 −1 1 −1 0.18
    17* 0 0 0 0 0.135
    18 1 1 1 −1 0.31
    19 0 1 0 0 0.198
    20 2 0 0 0 0.478
    21 −1 1 −1 −1 0.178
    22 0 0 0 0 0.154
    23 1 1 −1 1 0.326
    24 0 0 0 −2 0.124
    25* 0 0 0 0 0.117
    26* 0 0 0 0 0.112
    27 −1 −1 −1 −1 0.078
    28 1 −1 1 1 0.248
    29 −1 −1 1 1 0.09
    30 0 0 0 2 0.073
    Note: *indicates the central repeated trials.
    下载: 导出CSV

    表  4  灵敏度响应的方差分析

    Table  4.   Analysis of variance for sensitivity response

    SourceSum ‍of ‍squaresdfMean‍ squareF-valueP-valueStatus
    Model0.2877140.020532.91< 0.0001Significant
    $ {x}_{1} $-CNT0.193110.1931309.36< 0.0001
    $ {x}_{2} $-MLG0.011710.011718.7890.0006
    $ {x}_{3} $-Mixing time0.003610.00365.730.0302
    $ {x}_{4} $-Temperature0.010610.010617.020.0009
    $ {x}_{1}{x}_{2} $0.004710.00477.460.0155
    $ {x}_{1}{x}_{3} $0.003810.00386.110.0259
    $ {x}_{1}{x}_{4} $0.003610.00365.810.0292
    $ {x}_{2}{x}_{3} $0.006010.00609.680.0071
    $ {x}_{2}{x}_{4} $0.013610.013621.830.0003
    $ {x}_{3}{x}_{4} $0.000110.00010.22110.6449
    $ {{x}_{1}}^{2} $0.026710.026742.79< 0.0001
    $ {{x}_{2}}^{2} $0.008510.008513.650.0022
    $ {{x}_{3}}^{2} $0.000810.00081.260.2795
    $ {{x}_{4}}^{2} $0.003010.00304.880.0432
    Residual0.0094150.0006
    Lack of fit0.0079100.00082.700.1422Not significant
    Pure error0.001550.0003
    Cor total0.297029
    $ {R}^{2} $0.9685
    $ {R}_{\mathrm{A}\mathrm{d}\mathrm{j}}^{2} $0.9390
    $ {R}_{\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{d}}^{2} $0.8311
    C.V%14.86
    Adeq precision23.8126
    Notes: Cor total refers to the total number of correlation coefficients;$ {R}_{\mathrm{A}\mathrm{d}\mathrm{j}}^{2} $indicates adjusted coefficient of determination;$ {R}_{\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{d}}^{2} $ indicates predicted R-squared; Adeq precision is an important statistical metric for evaluating the predictive ability of response surface models.
    下载: 导出CSV

    表  5  SVR模型的5折交叉验证预测性能评价指标

    Table  5.   Performance evaluation index of SVR model by cross validation

    Index 1 2 3 4 5 Average
    $ {R}^{2} $ 0.9893 0.9841 0.9735 0.9769 0.9882 0.9824
    Rmse 0.0253 0.0328 0.0354 0.0389 0.0188 0.0320
    Mae 0.0193 0.0301 0.0231 0.0321 0.0151 0.0239
    下载: 导出CSV

    表  6  RSM模型的5折交叉验证预测性能评价指标

    Table  6.   Performance evaluation index of RSM model by cross validation

    Index 1 2 3 4 5 Average
    R2 0.9563 0.9642 0.9778 0.9669 0.9782 0.96868
    Rmse 0.04002 0.02701 0.03103 0.04731 0.03312 0.035698
    Mae 0.03623 0.02443 0.02671 0.03113 0.02608 0.028916
    下载: 导出CSV

    表  7  传感单元灵敏度性能对比

    Table  7.   Comparison of sensitivity performance of sensing units

    ReferenceMethodSensitivity /k$ \mathrm{P}{\mathrm{a}}^{-1} $Pressure range /$ \mathrm{k}\mathrm{P}\mathrm{a} $Comparison /k$ \mathrm{P}{\mathrm{a}}^{-1} $
    [12]Microstructure design0.4510-250.7665
    [42]Microstructure design0.690-101.7531
    [43]Microstructure design1.010-250.7665
    [44]mechanical blending0.38500-300.5371
    [45]Microstructure design0.3410-51.9698
    This studymechanical blending0.53710-30-
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-02-26
  • 修回日期:  2024-04-07
  • 录用日期:  2024-04-19
  • 网络出版日期:  2024-05-28

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