Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.
3D Printing Center, Johnson & Johnson, Miami, FL, 33126, USA.
Sci Rep. 2021 Oct 14;11(1):20424. doi: 10.1038/s41598-021-99959-6.
In this study, the effects of surface roughness and pore characteristics on fatigue lives of laser powder bed fusion (LPBF) Ti-6Al-4V parts were investigated. The 197 fatigue bars were printed using the same laser power but with varied scanning speeds. These actions led to variations in the geometries of microscale pores, and such variations were characterized using micro-computed tomography. To generate differences in surface roughness in fatigue bars, half of the samples were grit-blasted and the other half were machined. Fatigue behaviors were analyzed with respect to surface roughness and statistics of the pores. For the grit-blasted samples, the contour laser scan in the LPBF strategy led to a pore-depletion zone isolating surface and internal pores with different features. For the machined samples, where surface pores resemble internal pores, the fatigue life was highly correlated with the average pore size and projected pore area in the plane perpendicular to the stress direction. Finally, a machine learning model using a drop-out neural network (DONN) was employed to establish a link between surface and pore features to the fatigue data (logN), and good prediction accuracy was demonstrated. Besides predicting fatigue lives, the DONN can also estimate the prediction uncertainty.
本研究考察了表面粗糙度和孔隙特征对激光粉末床熔合（LPBF）Ti-6Al-4V 零件疲劳寿命的影响。使用相同的激光功率但不同的扫描速度打印了 197 个疲劳棒。这些操作导致微尺度孔隙的几何形状发生变化，并使用微计算机断层扫描对其进行了表征。为了在疲劳棒上产生表面粗糙度的差异，一半的样品进行了喷砂处理，另一半进行了机械加工。从表面粗糙度和孔隙统计的角度分析了疲劳行为。对于喷砂处理的样品，LPBF 策略中的轮廓激光扫描导致一个孔耗尽区将表面和具有不同特征的内部孔隔离。对于机械加工的样品，表面孔类似于内部孔，疲劳寿命与垂直于应力方向的平面上的平均孔径和投影孔面积高度相关。最后，使用辍学神经网络（DONN）建立了一个机器学习模型，将表面和孔隙特征与疲劳数据（logN）联系起来，并证明了良好的预测准确性。除了预测疲劳寿命外，DONN 还可以估计预测的不确定性。
