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 [1] 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%.

在本文中，已经对带缺口的样本和沙漏样本进行了由钨纳米粒子增强的聚氯丁二烯橡胶（CR）制成的超弹性橡胶状材料的多轴疲劳试验，用于限制球顶高度疲劳试验。基于单轴[1]和多轴疲劳实验，提出了半经验的ε-N疲劳模型，该模型可同时考虑材料各向异性和复杂应力状态，平均误差为20.7％。此外，已经将六个机器学习模型用于预测疲劳寿命，结果表明，深度神经网络最准确，平均误差等于14.3％。