期刊
INTERNATIONAL JOURNAL OF FATIGUE
卷 164, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ijfatigue.2022.107164
关键词
Powder metallurgy superalloy; Thermomechanical fatigue; Phase angle; Lifetime model; Machine learning method
资金
- National defense Basic research program [JCKY2019802B001]
- National Science Foundation of China [92060109]
This study investigates the thermomechanical fatigue behavior and life prediction of PM superalloy under various mechanical strain amplitudes and phase angles. A modified energy-based life prediction model is proposed by considering the effects of phase angle and tensile mean temperature. Machine learning methods are also used for life prediction.
In the present work, the thermomechanical fatigue behavior and life prediction of PM superalloy under various mechanical strain amplitudes and phase angles are investigated. Results demonstrate that the thermomechanical fatigue life is phase angle dependent and the tensile mean temperature is the main factor affecting the TMF life of alternative phase angle. A modified energy-based life prediction model is proposed by considering the effects of phase angle and tensile mean temperature. Moreover, machine learning methods were used for life prediction, and the TMF lifetime at various phase angles was predicted by combining it with RF regression and modified energy model.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据