Due to the nonlinear behavior of rubber for bushings, the prediction of mechanical properties of the bushing requires nonlinear finite element analysis (FEA) techniques and a lot of computation time. Therefore, we propose a method to efficiently predict the stiffness of bushings using an Artificial Neural Network (ANN) model trained by data from FEA. First, FEA was performed for the designed 3D and 2D bushing models. Based on the relationship between the bushing shape design variables and the stiffness values predicted by the FEA, we trained the Multilayer Perceptron (MLP) and the Convolutional Neural Network (CNN) models among the ANN models. Given the shape design variables of the bushing model, the stiffness values were predicted by the MLP model. Given the image of the bushing model, the stiffness values were predicted by the CNN model. The stiffness prediction results showed that both models can be used to predict the stiffness of the bushings, and that the CNN model is slightly more accurate than the MLP model. In particular, it is expected that designers can easily estimate stiffness values by taking advantage of the CNN model which can use photographic images of real parts as inputs.

由于用于衬套的橡胶的非线性行为，对衬套力学性能的预测需要非线性有限元分析（FEA）技术和大量的计算时间。因此，我们提出了一种使用由FEA数据训练的人工神经网络（ANN）模型有效预测衬套刚度的方法。首先，对设计的3D和2D套管模型执行FEA。基于衬套形状设计变量与FEA预测的刚度值之间的关系，我们在ANN模型中训练了多层感知器（MLP）和卷积神经网络（CNN）模型。给定衬套模型的形状设计变量，可以通过MLP模型预测刚度值。给定衬套模型的图像，通过CNN模型预测刚度值。刚度预测结果表明，两个模型都可以用于预测衬套的刚度，并且CNN模型比MLP模型更准确。特别是，期望设计人员可以利用CNN模型轻松估算刚度值，该模型可以将真实零件的摄影图像用作输入。