The development direction of high reliability and longer serviceable life for major equipment requires accurate fatigue life predictions of additively manufactured (AM) components. However, small samples and high scatter of fatigue performance have become significant challenges in accurately modeling the fatigue failure behavior of AM components. To overcome the limitation of traditional fatigue life prediction models, a multi-fidelity physics-informed machine learning (PIML) framework is proposed. In this framework, the uncertainty quantification of fatigue performance and the fitting low-fidelity fatigue data with physical consistency are achieved through a physics-guided Wasserstein generative adversarial network with gradient penalty (WGAN-GP). The introduced concept of transfer learning allows training a physics-informed neural network (PiNN) using multi-fidelity fatigue data during the training process. Embedding the effect of manufacturing defects on fatigue performance as physical constraints can ensure the physical consistency of the overall multi-fidelity framework. Compared with traditional neural network (NN) and PiNN, the multi-fidelity framework has significant advantages in strong prediction performance, generalization ability and effectiveness. Moreover, the results of deep feature transfer demonstrate that the proposed multi-fidelity framework is expected to be a unified fatigue life prediction framework for AM materials.

中文翻译：

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用于增材制造材料疲劳寿命预测的多保真物理信息机器学习框架


主要设备高可靠性和更长使用寿命的发展方向需要对增材制造 （AM） 组件进行准确的疲劳寿命预测。然而，小样本和高疲劳性能分散已成为精确模拟增材制造组件疲劳失效行为的重大挑战。为了克服传统疲劳寿命预测模型的局限性，该文提出一种多保真度物理信息机器学习 （PIML） 框架。在这个框架中，疲劳性能的不确定性量化和拟合具有物理一致性的低保真疲劳数据是通过物理引导的具有梯度惩罚的 Wasserstein 生成对抗网络 （WGAN-GP） 实现的。引入的迁移学习概念允许在训练过程中使用多保真疲劳数据训练物理信息神经网络 （PiNN）。将制造缺陷对疲劳性能的影响嵌入为物理约束可以确保整个多保真度框架的物理一致性。与传统神经网络 （NN） 和 PiNN 相比，多保真框架在较强的预测性能、泛化能力和有效性方面具有显著优势。此外，深度特征转移的结果表明，所提出的多保真度框架有望成为增材制造材料的统一疲劳寿命预测框架。