Prediction of Nonlinear Stress-strain Behaviors with Artificial Neural Networks and Its Application for Automotive Rubber Parts

This study presents a new method to predict the stress-strain curves of rubber materials using artificial neural networks in order to reduce the numbers of tensile tests and shows its application. Various stress-strain curves used for the machine learning are obtained by uniaxial, biaxial, planar tension tests on the chloroprene rubber specimens. Tests are carried out at a rate of 0.01 strain/s at 23 °C, and the Mullins effect is reflected through five load-unload processes in the strain range of 0 ∼ 20 %, 0 ∼ 50 %, 0 ∼ 70 %, and 0 ∼ 100 %. After training, the stress-strain relationships in untrained ranges are predicted. The predictions are compared with the experimental data in the strain range of 0 ∼ 100 %, which was previously reserved to confirm the prediction performance. It was predicted with errors within 0.04, 0.08, and 0.01 MPa for the uniaxial, biaxial, and planar tests, respectively. These small errors indicate predictions are reliable. For optimization of rubber parts, material constants of Ogden model are obtained using the predicted data in the strain of 0 ∼ 60 % and 0 ∼ 80 %. Dust covers are optimized to reduce stresses by the Taguchi method. The maximum von Mises stresses in the optimal designs are reduced by approximately 8 % and 14 %, compared to the initial ones.