This work presents a novel approach for defect based fatigue failure characterization using artificial intelligence (AI). An artificial neural network (ANN) is trained on experimentally determined data that is highly relevant in terms of fatigue. Load stress, hardness and killer defect size are the three main parameters defined as input arguments. Fatigue testing either reveals failure or non-failure, which represent the two possible output variables. Thus, every specimen subjected to this research work generates at least one data set. After total fracture occurs at a certain load level, killer defect size is evaluated by fracture surface analysis. The architecture as well as hyperparameters of the neural network are optimized by K-fold cross validation in order to obtain best prediction accuracy. Eventually, a conservative mean fatigue failure prediction accuracy of 91.6% is achieved. This unprecedented methodology is pioneering to predict fatigue failure without the need for extensive, error-prone, use of complex assessment methodologies and associated comprehensive expensive material testing. Without any expert-knowledge of evaluation procedures, developed AI-approach enables quick and reliable prediction of fatigue failure of machined components based on elementary key figures and shows prospective ways to revolutionize fatigue characterization.

这项工作提出了一种使用人工智能 (AI) 进行基于缺陷的疲劳失效表征的新方法。人工神经网络 (ANN) 是根据实验确定的与疲劳高度相关的数据进行训练的。载荷应力、硬度和致命缺陷尺寸是定义为输入参数的三个主要参数。疲劳测试揭示了失败或非失败，这代表了两个可能的输出变量。因此，接受这项研究工作的每个样本都至少生成一个数据集。在一定载荷水平下发生完全断裂后，通过断裂面分析评估致命缺陷尺寸。神经网络的架构和超参数通过 K 折交叉验证进行优化，以获得最佳预测精度。最终，达到了 91.6% 的保守平均疲劳失效预测精度。这种前所未有的方法是预测疲劳失效的先驱，而无需广泛、容易出错、使用复杂的评估方法和相关的综合昂贵的材料测试。无需任何评估程序的专家知识，开发的 AI 方法就可以根据基本关键数据快速可靠地预测加工部件的疲劳失效，并展示了彻底改变疲劳表征的前瞻性方法。