A Finite Element Model for Predicting the Static Strength of a Composite Hybrid Joint with Reinforcement Pins

Bianchi, Francesco; Liu, Yiding; Joesbury, Adam M.; Ayre, David; Zhang, Xiang

Abstract

This paper presents a finite element model for predicting the performance and failure behaviour of a hybrid joint assembling fibrous composites to a metal part with reinforcement micro pins for enhancing the damage tolerance performance. A unit-strip model using the cohesive elements at the bond interface is employed to simulate the onset and propagation of debonding cracks. Two different traction–separation laws for the interface cohesive elements are employed, representing the fracture toughness properties of the plain adhesive bond and a pin-reinforced interface, respectively. This approach can account for the large-scale crack-bridging effect of the pins. It avoids using concentrated pin forces in the numerical model, thus removing mesh-size dependency, and permitting more accurate and robust computational analysis. Lap joints reinforced with various pin arrays were tested under quasi-static load. Predicted load versus applied displacement relations are in good agreement with the test results, especially for the debonding onset and early stage of crack propagation.

Citation

Bianchi, F., Liu, Y., Joesbury, A. M., Ayre, D., & Zhang, X. (2023). A Finite Element Model for Predicting the Static Strength of a Composite Hybrid Joint with Reinforcement Pins. Materials, 16(9), 3297. https://doi.org/10.3390/ma16093297

• Journal Article Type: Article
• Acceptance Date: Apr 19, 2023
• Online Publication Date: Apr 22, 2023
• Publication Date: Apr 22, 2023
• Deposit Date: Apr 30, 2023
• Publicly Available Date: May 11, 2023
• Journal: Materials
• Electronic ISSN: 1996-1944
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 9
• Pages: 3297
• DOI: https://doi.org/10.3390/ma16093297
• Keywords: Article, composite hybrid joints, pin-reinforcement, static strength, FEA, experiment
• Public URL: https://nottingham-repository.worktribe.com/output/20008328