考虑材料参数不确定性的皮肤伤口缝合力预测模型

温广全; 纪小刚; 段玉顺; 邓霖

doi:10.21656/1000-0887.430067

考虑材料参数不确定性的皮肤伤口缝合力预测模型

doi: 10.21656/1000-0887.430067

温广全^1,,
纪小刚^{1, 2, ,},
段玉顺¹,
邓霖¹

1.
江南大学机械工程学院, 江苏无锡 214122
2.
江苏省食品先进制造装备技术重点实验室, 江苏无锡 214122

基金项目:

国家自然科学基金项目 52175234

国家自然科学基金项目 51105175

江苏省“六大人才高峰”项目 JXQC-006

详细信息

作者简介:
温广全(1998—)，男，硕士生(E-mail: wenguangquan163@163.com)

通讯作者:
纪小刚(1977—)，男，副教授，博士，硕士生导师(通讯作者. E-mail: bhearts@jiangnan.edu.cn)

中图分类号: O343.5
计量
- 文章访问数: 225
- HTML全文浏览量: 73
- PDF下载量: 44
- 被引次数: 0
出版历程
- 收稿日期: 2022-03-03
- 修回日期: 2022-06-05
- 刊出日期: 2023-04-01

A Prediction Model for Skin Wound Suture Forces With Uncertain Material Parameters

1.
School of Mechanical Engineering, Jiangnan University, Wuxi, Jiangsu 214122, P.R.China
2.
Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology, Wuxi, Jiangsu 214122, P.R.China

摘要

摘要: 为快速、有效地评估缝合皮肤伤口所需的力，运用非线性有限元方法，对不同尺寸、不同材料参数皮肤伤口进行缝合力数值计算；以计算结果为样本，采用椭球基神经网络模型，构建了皮肤伤口缝合力预测模型；考虑到皮肤材料参数的不确定性会影响数值计算结果的可靠性，预测模型采用Monte-Carlo方法进行了皮肤材料参数的不确定性传播分析；最后，以猪皮为实验材料进行伤口缝合力预测分析与测量实验，验证了该方法的可靠性.结果表明，间断缝合椭圆形皮肤伤口，缝合点处所需缝合力按缝合针次呈先增后减趋势，峰值力发生在伤口中线前，40 mm×10 mm伤口，缝合力峰值约为1.7 N；40 mm×14 mm伤口，缝合力峰值约为2.5 N.受材料参数不确定性影响，缝合力预测结果最高有±0.6 N的波动.构建预测模型的理论方法，为皮肤等生物软组织材料参数不确定性传播问题提供了有效的解决思路，同时为机器人手术缝合提供重要的力学参考信息.
- 皮肤力学特性 /
- 缝合力预测模型 /
- 椭球基神经网络 /
- 不确定性传播
Abstract: To evaluate the forces required for the suture of skin wounds quickly and effectively, the nonlinear finite element method was used to calculate the suture forces for skin wounds with different sizes and material parameters. With the calculated results as samples, the prediction model for skin wound suture forces was constructed by means of the EBF neural network model. Given the uncertain skin material parameters influencing the reliability of numerical results, the Monte-Carlo method was used to analyze the uncertainty propagation of skin material parameters. Finally, the prediction analysis and measuring experiment of wound suture forces were carried out with pig skin specimens to verify the reliability of the method. The results showed that, the suture force increases first and then decreases according to the suture point sequence, and the peak force occurs before the center of the wound. For a 40 mm×10 mm wound, the peak suture force is about 1.7 N, and that for a 40 mm×14 mm wound is about 2.5 N. Influenced by the uncertainty of material parameters, the prediction results of suture forces fluctuate by as much as ±0.6 N. The proposed theoretical prediction model provides an effective solution to the problem of parameter uncertainty propagation for biological soft tissue materials such as skins, and makes an important mechanical reference for robotic surgical suture.
- skin mechanics /
- prediction model for suture force /
- EBF neural network /
- uncertainty propagation

HTML全文

图 1 皮肤伤口轮廓

Figure 1. The skin wound profile

下载: 全尺寸图片幻灯片

图 2 皮肤伤口有限元简化模型

Figure 2. The simplified finite element model for the skin wound

下载: 全尺寸图片幻灯片

图 3 EBF神经网络基本结构

Figure 3. The basic structure of the EBF neural network

下载: 全尺寸图片幻灯片

图 4 猪皮单轴拉伸试样尺寸、试验设备及应力-应变曲线

Figure 4. Sizes, the test equipment and the stress-strain curve of the pig skin uniaxial tensile test specimen

下载: 全尺寸图片幻灯片

图 5 皮肤组织预应力释放的反向模拟边界条件及计算结果

Figure 5. Boundary conditions and calculation results of the reverse simulation of prestress release in the skin tissue

下载: 全尺寸图片幻灯片

图 6 伤口尺寸及缝合力测量方法

Figure 6. Measurement of wound sizes and suture forces

下载: 全尺寸图片幻灯片

图 7 伤口缝合力测量结果

注为了解释图中的颜色，读者可以参考本文的电子网页版本，后同.

Figure 7. Wound suture force measurement results

下载: 全尺寸图片幻灯片

图 8 有限元模拟缝合过程

Figure 8. The finite element simulation of the suture process

下载: 全尺寸图片幻灯片

图 9 3种代理模型拟合精度

Figure 9. The fitting precision of 3 proxy models

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图 10 输出响应概率分布图

Figure 10. The output the response probability distribution graph

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图 11 伤口缝合力预测结果与实验测量结果对比

Figure 11. The prediction results of wound suture forces compared with the experimental results

下载: 全尺寸图片幻灯片

表 1 人体皮肤材料参数取值范围

Table 1. Parameter value ranges of human skin materials

parameter	C₁₀/kPa	k₁/kPa	k₂	σ_X/kPa	σ_Y/kPa
value range	2.387~100.7	0.38~24 530	0.133~5 984.2	0~50	0~50

下载: 导出CSV

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