Mechanistic Study of Fatigue Crack Growth in Natural Rubber Composites Governed by Crack Growth Rate

ABSTRACT Fatigue failure of aircraft tires under high‐impact loads during take‐off and landing poses a critical challenge, with the crack growth rate of natural rubber (NR) composites serving as a key indicator of its fatigue performance. In this study, the crosslink density of NR composites was systematically tuned via vulcanization formulation to investigate its effect on fatigue crack growth behavior. Using a ramped fatigue testing method and in situ mechanical parameters, a non‐monotonic dependence of crack growth rate on crosslink density of the NR composites was revealed, which is attributed to the competing effects of strain‐induced crystallization and viscous hysteresis. A phenomenological model was developed by extending classical fracture theory, incorporating crosslink density and tear energy as governing variables. This model accurately captures the transition in the fatigue behavior of NR composites across different crosslinking regimes and enables rapid prediction of the optimal crosslink density for enhanced fatigue durability. These findings provide a mathematical model for improving the fatigue resistance of NR composites and new insights into the structure–property relationship of crystallizable rubber networks of the demanding aircraft tire.

Authors

Institutions

Publication Details

Journal
Polymer Engineering and Science
Published
2026-02-12
DOI
https://doi.org/10.1002/pen.70299
Primary Topic
Polymer Nanocomposites and Properties
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mechanistic Study of Fatigue Crack Growth in Natural Rubber Composites Governed by Crack Growth Rate

Weiya Lv, Benlong Su, Yuxuan Wang, Hao Wang et al.
Polymer Engineering and Science
Polymer Nanocomposites and Properties
article

Mechanistic Study of Fatigue Crack Growth in Natural Rubber Composites Governed by Crack Growth Rate

Weiya Lv, Benlong Su, Yuxuan Wang, Hao Wang, Jian Wu, Dandan Hou
article en

Abstract

ABSTRACT Fatigue failure of aircraft tires under high‐impact loads during take‐off and landing poses a critical challenge, with the crack growth rate of natural rubber (NR) composites serving as a key indicator of its fatigue performance. In this study, the crosslink density of NR composites was systematically tuned via vulcanization formulation to investigate its effect on fatigue crack growth behavior. Using a ramped fatigue testing method and in situ mechanical parameters, a non‐monotonic dependence of crack growth rate on crosslink density of the NR composites was revealed, which is attributed to the competing effects of strain‐induced crystallization and viscous hysteresis. A phenomenological model was developed by extending classical fracture theory, incorporating crosslink density and tear energy as governing variables. This model accurately captures the transition in the fatigue behavior of NR composites across different crosslinking regimes and enables rapid prediction of the optimal crosslink density for enhanced fatigue durability. These findings provide a mathematical model for improving the fatigue resistance of NR composites and new insights into the structure–property relationship of crystallizable rubber networks of the demanding aircraft tire.

Polymer Engineering and Science
Harbin Institute of Technology (CN), Tianjin Rubber Industry Institute (China) (CN), Zhejiang Energy Group (China) (CN), Heilongjiang Institute of Technology (CN)
Openalex Percentile: Top 57%
Polymer Nanocomposites and Properties
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.