Path-Dependent Progressive Failure Analysis for 3D-Printed Continuous Carbon Fibre Reinforced Composites

被引:0
|
作者
Chen, Yuan [1 ,2 ]
Ye, Lin [1 ,2 ]
机构
[1] Southern Univ Sci & Technol, Sch Syst Design & Intelligent Mfg SDIM, Shenzhen 518055, Peoples R China
[2] Southern Univ Sci & Technol, Shenzhen Key Lab Intelligent Mfg Continuous Carbon, Shenzhen 518055, Peoples R China
基金
澳大利亚研究理事会;
关键词
3D printing; Continuous carbon fibre; Modelling; Energy absorption; Negative Poisson's ratio; OPTIMIZATION;
D O I
10.1186/s10033-024-01054-0
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
In order to predict the damage behaviours of 3D-printed continuous carbon fibre (CCF) reinforced composites, when additional short carbon fibre (SCF) composite components are employed for continuous printing or special functionality, a novel path-dependent progressive failure (PDPF) numerical approach is developed. First, a progressive failure model using Hashin failure criteria with continuum damage mechanics to account for the damage initiation and evaluation of 3D-printed CCF reinforced polyamide (PA) composites is developed, based on actual fibre placement trajectories with physical measurements of 3D-printed CCF/PA constituents. Meanwhile, an elastic-plastic model is employed to predict the plastic damage behaviours of SCF/PA parts. Then, the accuracy of the PDPF model was validated so as to study 3D-printed CCF/PA composites with either negative Poisson's ratio or high stiffness. The results demonstrate that the proposed PDPF model can achieve higher prediction accuracies in mechanical properties of these 3D-printed CCF/PA composites. Mechanism analyses show that the stress distribution is generally aggregated in the CCF areas along the fibre placement paths, and the shear damage and matrix tensile/compressive damage are the key damage modes. This study provides a new approach with valuable information for characterising complex 3D-printed continuous fibre-matrix composites with variable mechanical properties and multiple constituents.
引用
收藏
页数:10
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