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arXiv 2109.09655cond-mat.mtrl-scics.LGphysics.app-ph
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基于神经网络模型的表面和孔隙特征对Laser粉末床熔凝Ti-6Al-4V合金疲劳寿命的影响

Impact of Surface and Pore Characteristics on Fatigue Life of Laser Powder Bed Fusion Ti-6Al-4V Alloy Described by Neural Network Models

University of Notre Dame(圣母大学);Johnson & Johnson(强生);Rolls-Royce Corporation(罗尔斯·罗伊斯公司)

Seunghyun Moon, Ruimin Ma, Ross Attardo, Charles Tomonto, Mark Nordin, Paul Wheelock, Michael Glavicic, Maxwell Layman, Richard Billo, Tengfei Luo

摘要:

在这项研究中,研究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197个疲劳条。这些作用导致微尺度孔隙的几何形状的变化,并且使用微计算机断层成像来表征这种变化。为了在疲劳棒中产生表面粗糙度的差异,对一半样品进行喷砂处理,另一半进行机加工。疲劳行为进行了分析,相对于表面粗糙度和统计的孔隙。对于喷砂处理的样品,在LPBF策略中的轮廓激光扫描导致了具有不同特征的表面和内部孔的孔耗尽区隔离。对于机械加工的样品,其中表面孔类似于内部孔,疲劳寿命与平均孔径和垂直于应力方向的平面中的投影孔面积高度相关。最后,使用脱落神经网络(DONN)的机器学习模型建立了表面和孔隙特征与疲劳数据(logN)之间的联系,并证明了良好的预测精度。除了预测疲劳寿命外,DONN还可以估计预测的不确定性。

英文摘要:

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 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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