This paper presents a hybrid-driven probabilistic damage assessment approach by considering creep-fatigue-oxidation damage interaction (CFO-DI). Based on generalized strain energy density exhaustion (GSEDE) framework, the hybrid-driven concept integrates the strengths of both physics-based models and machine learning, exploring the frontier from deterministic evaluation to probabilistic assessment. Experimental investigations involving generalized creep-fatigue loading tests are conducted to establish a comprehensive dataset in Inconel 718 at 650 °C. Deterministic models for fatigue, creep, and oxidation damages are developed, and their interactions are analyzed using the GSEDE framework. To tackle limited experimental data, a divide-and-conquer strategy employing machine learning models is implemented for data augmentation. Probabilistic assessments are performed incorporating uncertainties from material properties, loading conditions, and model parameters using Monte Carlo simulations and Latin Hypercube Sampling. The results demonstrate accurate life prediction accuracy and reliable probability distributions in the presence of oxidation damage. Finally, a novel three-dimensional probabilistic CFO-DI assessment diagram quantified by the confidence level is developed, providing a technical pathway for safe-life design in high-temperature structural applications.

中文翻译：

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蠕变-疲劳-氧化相互作用的混合驱动概率损伤评估


本文通过考虑蠕变-疲劳-氧化损伤相互作用 （CFO-DI） 提出了一种混合驱动的概率损伤评估方法。基于广义应变能密度耗尽 （GSEDE） 框架，混合驱动概念融合了基于物理的模型和机器学习的优势，探索了从确定性评估到概率评估的前沿领域。进行了涉及广义蠕变疲劳载荷试验的实验研究，以在 650 °C 下建立 Inconel 718 的综合数据集。 开发了疲劳、蠕变和氧化损伤的确定性模型，并使用 GSEDE 框架分析了它们的相互作用。为了处理有限的实验数据，实施了采用机器学习模型的分而治之策略来增强数据。使用 Monte Carlo 模拟和拉丁超立方采样，结合材料特性、载荷条件和模型参数的不确定性进行概率评估。结果表明，在存在氧化损伤的情况下，准确的寿命预测精度和可靠的概率分布。最后，开发了一种由置信度量化的新型三维概率 CFO-DI 评估图，为高温结构应用中的安全寿命设计提供了一条技术途径。