# Prediction of Bending Mechanical Behaviors of SiCf/SiC 2.5D Woven Composites with Random Pore Defects

**Authors:** Xiaomeng Wang, Tiantian Yang, Ling Wang, Weijie Xie, Kun Qian, Mingwei Chen, Haipeng Qiu, Diantang Zhang

PMC · DOI: 10.3390/ma19050934 · 2026-02-28

## TL;DR

This paper introduces a model that predicts how SiCf/SiC composites bend and break, considering random pore defects that affect their strength.

## Contribution

A novel finite element model incorporating random pore defects to predict bending behavior in SiCf/SiC composites.

## Key findings

- The model accurately predicts bending strength with a 4.6% relative error compared to experiments.
- Random pore defects significantly influence mechanical damage behavior in the composites.
- Micro-CT data was used to inform the statistical representation of yarns and pores in the model.

## Abstract

The inevitable pore defects generated in the preparation process have a great impact on the mechanical properties of the ceramic matrix composites. However, the pore defects on the composites were ignored to a large extent in models established in the previous research. In this study, in order to investigate the bending damage behaviors of SiCf/SiC (SiC fiber-reinforced SiC matrix) angle-interlock (2.5D) woven composites prepared by the precursor immersion pyrolysis (PIP) method, a more precise full-scale model of composites was established by finite element (FE) method with taking into account of random pore defects generated by Monte Carlo algorithm. Micro-computed tomography (Micro-CT) was employed to acquire the statistical data of the yarns and pores of SiCf/SiC 2.5D woven composites. A bending test was conducted to study the damage behaviors of the composite and compared with the prediction of the FE model. The result shows that the proposed model with random pores can predict the mechanical damage behavior of SiCf/SiC 2.5D woven composites effectively under three-point bending. The simulated bending strength shows a good agreement with the experimental data, with a relative error of approximately 4.6%.

## Full-text entities

- **Chemicals:** SiCf (-), SiC (MESH:C022088)

## Figures

7 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12986451/full.md

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Source: https://tomesphere.com/paper/PMC12986451