Hou Junling, Lu Xuan, Zhang Kaining, Jing Yidong, Zhang Zhenjie, You Junfeng, Li Qun
State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Research Institute of Xi'an Jiaotong University, Hangzhou 311215, China.
Materials (Basel). 2022 May 25;15(11):3776. doi: 10.3390/ma15113776.
In this study, we present a systematic scheme to identify the material parameters in constitutive model of hyperelastic materials such as rubber. This approach is proposed based on the combined use of general regression neural network, experimental data and finite element analysis. In detail, the finite element analysis is carried out to provide the learning samples of GRNN model, while the results observed from the uniaxial tensile test is set as the target value of GRNN model. A problem involving parameters identification of silicone rubber material is described for validation. The results show that the proposed GRNN-based approach has the characteristics of high universality and good precision, and can be extended to parameters identification of complex rubber-like hyperelastic material constitutive.
在本研究中，我们提出了一种系统方案来识别橡胶等超弹性材料本构模型中的材料参数。该方法是基于广义回归神经网络、实验数据和有限元分析的联合使用而提出的。具体而言，进行有限元分析以提供GRNN模型的学习样本，而将单轴拉伸试验观察到的结果设置为GRNN模型的目标值。描述了一个涉及硅橡胶材料参数识别的问题以进行验证。结果表明，所提出的基于GRNN的方法具有通用性高和精度好的特点，并且可以扩展到复杂橡胶类超弹性材料本构的参数识别。
