Work

Fatigue Life Prediction and Damage Mechanism Analysis of 2.5D Woven Composites Employing Multiple Statistical Volume Elements

Year: 2025

Type: article

Abstract: ABSTRACT The fatigue performance of 2.5D woven composites (2.5DWC) remains difficult to predict due to the inherent randomness of yarn geometry introduced during manufacturing. In this work, a stochastic fatigue life prediction framework is established based on multiple statistical volume elements (SVEs) to incorporate geometric variability into mesoscale simulations. Each SVE represents a random realization of yarn architecture, enabling statistical evaluation of fatigue behavior across unit‐cell populations. A fatigue progressive damage model coupled with a modified Hashin criteria is implemented and validated against experimental fatigue data. The predicted fatigue lifetimes from six SVEs fall within a 3× error band of the experimental results, accurately reproducing the statistical scatter of fatigue life observed in tests. Damage evolution analysis reveals three distinct fatigue stages, that is, initial accumulation, rapid propagation, and catastrophic failure, dominated by coupled matrix cracking and yarn fracture. Thermal imaging further confirms a phased energy release process consistent with the simulated evolution. The proposed multiple SVE framework effectively bridges deterministic modeling and experimental variability, offering a robust tool for reliability‐based fatigue design of woven composite structures.

Source: Polymer Composites

DOI: 10.1002/pc.70773

Authors: Chao Pan, Junhua Guo, Cihang Jiang, Huabing Wen, Wantao Guo, Weidong Wen

Institutions: Jiangsu University of Science and Technology, Luoyang Ship Material Research Institute, Nanjing University of Aeronautics and Astronautics

Language: English

Topic: Mechanical Behavior of Composites

Subfield: Mechanics of Materials

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, China Postdoctoral Science Foundation, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province

Awards: G1426480701, G4417054110, G4650113908