Multiaxial fatigue life prediction of polychloroprene rubber (CR) reinforced with tungsten nano-particles based on semi-empirical and machine learning models

In this paper, multiaxial fatigue experiments on a hyperelastic rubber-like material made of polychloroprene rubber (CR) reinforced with tungsten nano-particles have been carried out on notched specimens and hourglass specimens, utilized for limiting dome height fatigue tests. Based on the uniaxial (Choi et al., 2020) and multiaxial fatigue experiments, a semi-empirical ε-N fatigue model is proposed, allows accounting for both material anisotropy and complex stress states, showing an average error of 20.7%. Furthermore, six machine learning models have been employed for the fatigue life prediction and shown that the Deep Neural Network is the most accurate, with an average error equal to 14.3%.