克里金
高斯过程
巴黎法
波纹度
替代模型
回归
回归分析
计算机科学
算法
高斯分布
人工智能
机器学习
结构工程
数学
工程类
机械工程
断裂力学
统计
物理
裂缝闭合
量子力学
标识
DOI:10.1016/j.tafmec.2024.104278
摘要
Recent advancements in computational capabilities have made data-driven approaches increasingly valuable for accurately simulating complex engineering phenomena. These approaches provide mathematical approximations that serve as surrogate models, replacing complex explicit mathematical relationships with significantly reduced computational demands. In this study, we propose the development of a surrogate model to predict the crack growth behavior of aluminum 7075-T6, a commonly used material in the aviation industry. The crack growth behavior of this alloy is complex and challenging to describe with explicit mathematical equations due to multiple influencing factors. Notably, the "waviness" behavior observed in the linear Paris law region, coupled with the significant impact of the R-ratio and specimen thickness, complicates crack growth predictions. To address this complexity, four different machine learning regression algorithms are studied: Random Forest (RF), Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), and Kernel Regression (KR). Their performance is evaluated and the most promising algorithm is selected to predict the crack growth of aluminum 7075-T6 under various fatigue stress spectra.
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