Microstructural defects and inhomogeneity of titanium alloys fabricated by laser powder bed fusion (LPBF) make their fatigue behaviors much more complicated than the conventionally made ones, especially in very-high-cycle fatigue (VHCF) regime. Most of traditional models/formulae and currently-used machine learning algorithms mainly concern fatigue behavior of LPBF-fabricated titanium alloys in high-cycle fatigue (HCF) regime, but rarely in VHCF regime. In this paper, a deep belief neural network-back propagation (DBN-BP) model was proposed to predict the fatigue life of LPBF-fabricated Ti-6Al-4V up to VHCF regime. Results obtained in this study indicate that the DBN-BP model exhibits high precision and strong stability in predicting the fatigue life of LPBF-fabricated Ti-6Al-4V in both HCF and VHCF regimes. The primary hyperparameters of the DBN-BP model were optimized to further improve the prediction precision of this innovative model. Finally, the optimal DBN-BP model was applied to predict the relation between mean stress and stress amplitude, and the effect of energy density on the fatigue behavior of LPBF-fabricated Ti-6Al-4V up to VHCF regime.

通过激光粉末床熔合 (LPBF) 制造的钛合金的微观结构缺陷和不均匀性使其疲劳行为比传统制造的钛合金复杂得多，特别是在甚高周疲劳 (VHCF) 状态下。大多数传统模型/公式和当前使用的机器学习算法主要关注 LPBF 制造的钛合金在高周疲劳 (HCF) 状态下的疲劳行为，但很少关注 VHCF 状态下的疲劳行为。在本文中，提出了一种深度置信神经网络反向传播 (DBN-BP) 模型来预测 LPBF 制造的 Ti-6Al-4V 在 VHCF 状态下的疲劳寿命。在这项研究中获得的结果表明，DBN-BP 模型在预测 LPBF 制造的 Ti-6Al-4V 在 HCF 和 VHCF 状态下的疲劳寿命方面表现出高精度和强稳定性。对 DBN-BP 模型的主要超参数进行了优化，进一步提高了该创新模型的预测精度。最后，应用最佳 DBN-BP 模型预测平均应力与应力幅值之间的关系，以及能量密度对 LPBF 制造的 Ti-6Al-4V 直至 VHCF 状态的疲劳行为的影响。