A Novel Approach for Identifying Hyper-Elastic Material Parameters of Cartilage based on FEM and Neural Networks

Abstract

Cartilage damage and degeneration may lead to osteoarthritis for both animals and humans. Quantitative studies on the nonlinear hyper-elastic behavior of cartilages are essential to evaluate cartilage tissue deterioration. However, direct identification of the material behavior is not feasible. This paper presents a procedure to characterize the nonlinear mechanical behavior of the cartilage tissue by an inverse method using measurable structural quantities. First, a two-way neural network (NN) is established, which uses the fully trained forward problem neural network instead of the forward problem solver to generate training samples for inverse problem neural network. Moreover, based on the experimental data of the kangaroo shoulder joint, a nonlinear finite element (FE) model is then created to produce a dataset for training the forward network. Furthermore, intensive studies are conducted to examine the performance of our two-way NN method for the prediction of cartilage hyper-elastic material parameters by comparison with the direct inverse NN method. When only the direct inverse problem neural network is used for training, all samples are from FE simulations and the simulation time is 50.7 h, and the prediction time is tens of seconds. Besides, our two-way neural network calls the trained forward NN to collect training samples, and all the samples can be obtained in seconds, with which the simulation time is only 78 s. The predicted results are in good agreement with the experimental data, and the comparison shows that our two-way NN is an efficient and proficient method to predict the parameters for other biological soft tissues.