Failure envelope prediction of 2D SiC f /SiC composites based on XGBoost model

被引:3
作者
Wang, Ben [1 ,2 ,3 ]
Zhao, Jingyu [4 ]
Guo, Zaoyang [5 ]
Wang, Biao [1 ,2 ,3 ,6 ]
机构
[1] Dongguan Univ Technol, Res Inst Interdisciplinary Sci, Dongguan 523808, Guangdong, Peoples R China
[2] Dongguan Univ Technol, Sch Mat Sci & Engn, Dongguan 523808, Guangdong, Peoples R China
[3] Guangdong Prov Key Lab Extreme Condit, Dongguan 523803, Peoples R China
[4] Syst Design Inst Mech Elect Engn, Beijing 100854, Peoples R China
[5] Harbin Inst Technol, Sch Sci, Shenzhen 518055, Guangdong, Peoples R China
[6] Sun Yat Sen Univ, Sino French Inst Nucl Engn & Technol, Zhuhai 519082, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Ceramic-matrix composites (CMCs); Defects; Computational modelling; Failure; DAMAGE MODEL; PART I; DELAMINATION; DEFORMATION; STRENGTH; BEHAVIOR; CRITERIA; TUBES;
D O I
10.1016/j.compositesa.2024.108287
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this paper, a multiple failure mechanisms -based progressive damage model is developed to capture the mechanical response and defect -induced nonlinear behavior of 2D SiC f /SiC composites. This model is then adopted to simulate the stress - strain response of a representative volume element model under uniaxial loading, showing excellent agreement with experimental results. The effects of the volume fraction of SiC fibers, as well as those of the porosity and microcrack density of SiC matrix, on the deformation and damage behaviors of SiC f /SiC composites are predicted. Based on this, the failure envelopes related to micro-mesoscopic characteristics under combined loadings are generated as the database for the eXtreme Gradient Boosting (XGBoost) model training. Finally, a data -driven failure model is established for 2D SiC f /SiC composites, whose prediction is compared with the formal failure criteria. The results demonstrate that the data -driven 2D SiC f /SiC composite failure model is reliable in constructing the failure criteria related to micro-mesoscopic features.
引用
收藏
页数:14
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