Prediction of Nonlinear Stiffness of Automotive Bushings by Artificial Neural Network Models Trained by Data from Finite Element Analysis

• Published in: International journal of automotive technology 2020, 21(6), 118, pp.1539-1551
• Main Authors: Jung, Yeon-Woo, Kim, Heung-Kyu
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Automotive Engineering; Bushings; Design; Engineering; Finite element analysis; Finite element method; Mechanical properties; Multilayer perceptrons; Neural networks; Nonlinear analysis; Predictions; Stiffness; Three dimensional models; Two dimensional models; 자동차공학
• ISSN: 1229-9138; 1976-3832
• DOI: 10.1007/s12239-020-0145-1

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.