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陶雅萍, 桑建兵, 孙丽芳, 向华鑫, 杨鹏, 史玉龙. 数据驱动下人体面部皮肤的本构参数反演及力学特征分析. 力学学报, 2024, 56(6): 1-11. DOI: 10.6052/0459-1879-24-018
引用本文: 陶雅萍, 桑建兵, 孙丽芳, 向华鑫, 杨鹏, 史玉龙. 数据驱动下人体面部皮肤的本构参数反演及力学特征分析. 力学学报, 2024, 56(6): 1-11. DOI: 10.6052/0459-1879-24-018
Tao Yaping, Sang Jianbing, Sun Lifang, Xiang Huaxin, Yang Peng, Shi Yulong. Inverse estimation of constitutive parameters and mechanical characterization of human facial skin under data-driven approach. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(6): 1-11. DOI: 10.6052/0459-1879-24-018
Citation: Tao Yaping, Sang Jianbing, Sun Lifang, Xiang Huaxin, Yang Peng, Shi Yulong. Inverse estimation of constitutive parameters and mechanical characterization of human facial skin under data-driven approach. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(6): 1-11. DOI: 10.6052/0459-1879-24-018

数据驱动下人体面部皮肤的本构参数反演及力学特征分析

INVERSE ESTIMATION OF CONSTITUTIVE PARAMETERS AND MECHANICAL CHARACTERIZATION OF HUMAN FACIAL SKIN UNDER DATA-DRIVEN APPROACH

  • 摘要: 人体面部皮肤的本构参数反演及力学特性研究对皮肤病变早期诊断、皮肤仿生材料设计及计算图形学中的面部模型建立都有着至关重要的作用, 机器学习与有限元仿真方法相结合能更高效、更准确地解决非入侵式皮肤组织本构参数反求问题. 首先建立了面部皮肤多向拉伸下的应力松弛有限元模型, 并通过多步位移控制法对皮肤的超弹性力学性能与黏弹性力学性能分离; 对Gasser-Ogden-Holzapfel(GOH)与Prony series本构模型参数进行敏感性分析, 揭示影响面部皮肤应力松弛实验结果的关键参数. 其次, 利用贝叶斯超参数优化理论搭建随机森林(RF)模型与支持向量机(SVR)模型, 结合实验数据对人体面部皮肤组织本构参数进行了反求. 最后, 将计算得到的有限元仿真曲线与试验获得的拉伸力响应曲线对比, 并引入决定系数 R2对两种模型的预测准确性进行了评估. 结果表明, 纤维组织分散度κ、剪切模量相关参数C10和松弛模量g1是影响皮肤应力松弛实验结果的关键参数, RF模型数值计算曲线与试验曲线的拟合优度为0.98, 其在皮肤本构参数反演问题上表现出更高的准确率, 机器学习可以精准高效地获取面部皮肤的本构参数, 进而准确描述皮肤组织的力学性能, 该方法也可进一步推广到其他生物软组织的复杂本构参数反演问题.

     

    Abstract: Research on the inverse identification of constitutive parameters and mechanical properties of human facial skin plays a crucial role in early diagnosis of skin lesions, design of biomimetic materials for skin, and establishment of facial models in computer graphics. The combination of machine learning and finite element simulation methods can provide a more efficient and accurate solution to the non-invasive inverse problem of skin tissue constitutive parameters. In this study, a finite element model of facial skin under multi-directional stretching was established, and the stress relaxation behavior of skin was separated into hyperelastic and viscoelastic mechanical properties using the multi-step displacement control method. The sensitivity analysis of the Gasser-Ogden-Holzapfel (GOH) and Prony series constitutive model parameters was conducted to reveal the key parameters that affect the results of facial skin stress relaxation experiments. Furthermore, by utilizing Bayesian hyperparameter optimization theory, a Random Forest (RF) model and a support vector machine (SVR) model were constructed to inversely determine the constitutive parameters of human facial skin tissue based on experimental data. Finally, the computed finite element simulation curves were compared with the experimental stress-strain response curves, and the coefficient of determination (R2) was introduced to evaluate the predictive accuracy of the two models. The results showed that fiber tissue dispersion coefficient κ, shear modulus related parameters C10, and Relaxation modulus g1 were the key parameters influencing the results of skin stress relaxation experiments. The RF model achieved a goodness of fit of 0.98 between the numerical computation curve and the experimental curve, demonstrating higher accuracy in the inverse identification of skin constitutive parameters. Machine learning can accurately and efficiently obtain the constitutive parameters of facial skin, thus accurately describing the mechanical properties of skin tissue. This method can also be further extended to the complex inverse problem of constitutive parameter identification in other biological soft tissues.

     

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