Determination of cohesive parameters for fibre-reinforced composite interfaces based on finite element analysis and machine learning. (November 2022)

• Record Type: Journal Article
• Authors: Yuan, Mingqing; Zhao, Haitao; Liu, Shen; Ren, Hantao; Zhang, Boming; Sun, Xinyang; Chen, Ji'an
• Is Part Of: Journal of composite materials. Volume 56:Number 27(2022)
• Page Start: 4113
• Page End: 4122
• Publication Date: 2022-11
• Subjects: Microdroplet test -- cohesive parameter -- finite element analysis -- machine learning -- kernel ridge regression; Composite materials -- Periodicals; Composites -- Périodiques; 620.118
• DOI: 10.1177/00219983221127400
• Languages: English
• ISSNs: 0021-9983

Abstract

This paper proposed a method for determining the cohesive parameters of fibre-reinforced composite interfaces based on finite element analysis (FEA) and machine learning. 3D FEA models with different boundary conditions and 2D FEA models were created to simulate the process of microdroplet tests, and to compare their maximum reaction forces and the time costs. The proper FEA model that is accurate and efficient was adopted to establish the data set of machine learning. Machine learning based on the FEA data set was divided into two steps: feature selection and kernel ridge regression (KRR) prediction. Feature selection was carried out to confirm the validity of the features, to obtain the optimal parameter of KRR prediction and to quantitatively illustrate the effect of the cohesive parameters on the maximum reaction force. Interfacial shear strength (IFSS) and interfacial fracture toughness (IFFT) of a poly ( p -phenylene benzobisoxazole) (PBO) fibre-reinforced epoxy composite were successfully predicted by the KRR method without extra mechanical theories or assumptions.