The prediction of fatigue life is essential in the development of products to avoid unexpected failures during their useful life. Although different linear and nonlinear damage accumulation approaches have been proposed, no model has been as universally used as Miner’s linear damage rule due to its simplicity and life prediction results. Discrepancies in the prediction of fatigue life are present within the manufacturing process, which is generated from the material through the manufacturing process and during applied loads. Owing to new design application areas, such as in biomedical devices and the aerospace industry, among others, the development of new ways to reduce errors in predicting fatigue has become an increasing necessity. This paper addresses fatigue life prediction improvement when if performed through a combination of synthetic data an artificial neural networks (ANNs). The novelty of this work is based on the proposal and validation of virtual synthetic fatigue data as a complementary input parameter in the ANN. For the design of the ANN, 116 experimental results of nodular cast iron direction knuckles were analyzed. As seen during the validation process, the employment of synthetic data as input increased significantly the forecast of the ANN.

疲劳寿命的预测对于产品开发至关重要，以避免产品在使用寿命期间发生意外故障。尽管已经提出了不同的线性和非线性损伤累积方法，但是由于其简单性和寿命预测结果，没有一种模型能够像Miner的线性损伤规则那样普遍使用。疲劳寿命预测中的差异存在于制造过程中，这是由材料在整个制造过程中以及施加的载荷期间产生的。由于新的设计应用领域，例如在生物医学设备和航空航天工业中的应用，开发新的方法来减少预测疲劳的误差已变得越来越必要。如果通过综合人工神经网络（ANN）的组合执行疲劳寿命预测，则本文可以解决这一问题。这项工作的新颖性基于虚拟人工疲劳数据作为ANN中的补充输入参数的建议和验证。对于人工神经网络的设计，分析了球墨铸铁方向节的116个实验结果。正如在验证过程中看到的那样，使用综合数据作为输入大大提高了人工神经网络的预测。