论文标题
疲劳强度估计的稳健性
Robustness in Fatigue Strength Estimation
论文作者
论文摘要
疲劳强度估计是一个昂贵的手动材料表征过程，其中最先进的方法遵循标准化的实验和分析程序。在本文中，我们研究了一种基于机器学习的模块化方法，以进行疲劳强度估计，该方法可能会减少实验数量，从而减少整体实验成本。尽管具有很高的潜力，但在现实生活实验室中的新方法的部署比理论定义和仿真还需要更多。因此，我们研究了方法对指定载荷的先验和离散化的错误指定的鲁棒性。我们确定其适用性及其在最新方法上的有利行为，有可能减少昂贵的实验数量。
Fatigue strength estimation is a costly manual material characterization process in which state-of-the-art approaches follow a standardized experiment and analysis procedure. In this paper, we examine a modular, Machine Learning-based approach for fatigue strength estimation that is likely to reduce the number of experiments and, thus, the overall experimental costs. Despite its high potential, deployment of a new approach in a real-life lab requires more than the theoretical definition and simulation. Therefore, we study the robustness of the approach against misspecification of the prior and discretization of the specified loads. We identify its applicability and its advantageous behavior over the state-of-the-art methods, potentially reducing the number of costly experiments.
