Wu Hao, Wang Anbin, Gan Zhiqiang, Gan Lei
School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China.
School of Science, Harbin Institute of Technology, Shenzhen 518055, China.
Materials (Basel). 2024 Dec 24;18(1):11. doi: 10.3390/ma18010011.
Fatigue failure poses a serious challenge for ensuring the operational safety of critical components subjected to cyclic/random loading. In this context, various machine learning (ML) models have been increasingly explored, due to their effectiveness in analyzing the relationship between fatigue life and multiple influencing factors. Nevertheless, existing ML models hinge heavily on numeric features as inputs, which encapsulate limited information on the fatigue failure process of interest. To cure the deficiency, a novel ML model based upon convolutional neural networks is developed, where numeric features are transformed into graphical ones by introducing two information enrichment operations, namely, Shapley Additive Explanations and Pearson correlation coefficient analysis. Additionally, the attention mechanism is introduced to prioritize important regions in the image-based inputs. Extensive validations using experimental results of two laser powder bed fusion-fabricated metals demonstrate that the proposed model possesses better predictive accuracy than conventional ML models.
疲劳失效对确保承受循环/随机载荷的关键部件的运行安全构成了严峻挑战。在此背景下，由于各种机器学习（ML）模型在分析疲劳寿命与多个影响因素之间的关系方面具有有效性，因此对其进行了越来越多的探索。然而，现有的ML模型严重依赖数值特征作为输入，而这些数值特征所包含的关于感兴趣的疲劳失效过程的信息有限。为了弥补这一缺陷，开发了一种基于卷积神经网络的新型ML模型，通过引入两种信息增强操作，即夏普利值（Shapley Additive Explanations）和皮尔逊相关系数分析，将数值特征转换为图形特征。此外，引入了注意力机制对基于图像的输入中的重要区域进行优先级排序。使用两种激光粉末床熔融制造金属的实验结果进行的大量验证表明，所提出的模型比传统ML模型具有更好的预测准确性。
