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Digital twin-driven framework for fatigue life prediction of welded structures considering residual stress

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成果类型:
期刊论文
作者:
Peng, Anyin;Ma, Yafei;Huang, Ke;Wang, Lei
通讯作者:
Ma, YF
作者机构:
[Huang, Ke; Ma, Yafei; Peng, Anyin; Wang, Lei; Ma, YF] Changsha Univ Sci & Technol, Sch Civil Engn, Changsha 410114, Peoples R China.
通讯机构:
[Ma, YF ] C
Changsha Univ Sci & Technol, Sch Civil Engn, Changsha 410114, Peoples R China.
语种:
英文
关键词:
Fatigue crack growth;Welding residual stress;Weight function method;Digital twin;Dynamic Bayesian network
期刊:
International Journal of Fatigue
ISSN:
0142-1123
年:
2024
卷:
181
页码:
108144
基金类别:
National Key Research and Development Program of China [2021YFB2600900]; National Natural Science Foundation of China [52378124, 52178107]; Postgraduate Scientific Research Innovation Project of Hunan Province [CX20230839]
机构署名:
本校为第一且通讯机构
院系归属:
土木工程学院
摘要:
The welding of steel generates substantial welding residual stress (WRS), which exerts a significant impact on the fatigue life of steel bridges. In this study, a physical model for calculating the fatigue crack growth (FCG) life of welded specimens in the WRS field is established based on the weight function method. Experimental data validates the reliability and precision of the proposed physical model. The impact of WRS on the fatigue life of structural components is scrutinized and analyzed. On this basis, a digital twin (DT) framework driven by a physical-data model is proposed to conside...

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