Few machine learning models are applied to investigate the influence of defect features on very-high-cycle fatigue performance of additively manufactured alloys and these models usually suffer from data scarcity. Interpolation methods are run to enlarge dataset size and machine learning models are established to investigate the synergic influence of layer thickness, stress ratio, stress amplitude, defect size, shape and location on fatigue life of selective laser melted AlSi10Mg. Results show that the increases in defect distance to surface, circularity, and layer thickness favor higher fatigue life; however, the increases in stress amplitude, stress ratio, and defect size decrease fatigue life.

很少有机器学习模型用于研究缺陷特征对增材制造合金的超高周疲劳性能的影响，并且这些模型通常缺乏数据。运行插值方法以扩大数据集大小，并建立机器学习模型来研究层厚度、应力比、应力幅值、缺陷尺寸、形状和位置对选择性激光熔化 AlSi10Mg 疲劳寿命的协同影响。结果表明，缺陷到表面的距离、圆度和层厚度的增加有利于提高疲劳寿命；然而，应力幅、应力比和缺陷尺寸的增加会降低疲劳寿命。