ArticleMaterials (Basel, Switzerland)2023
A Methodology to Predict the Fatigue Life under Multi-Axial Loading of Carbon Fiber-Reinforced Polymer Composites Considering Anisotropic Mechanical Behavior.
Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Machine learning analysis based on deep learning for fatigue diagnostics in carbon fiber reinforced polymers.PloS one · 2026Article
- Article
- Prediction of Short- to Long-Term Cyclic Deformation Behavior and Fatigue Life of Polymers.Polymers · 2024Article
- Review
- Fatigue Life Prediction for Injection-Molded Carbon Fiber-Reinforced Polyamide-6 Considering Anisotropy and Temperature Effects.Materials (Basel, Switzerland) · 2024Article
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4 authors.
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Abstract
Carbon fiber-reinforced polymers (CFRP) have been actively employed as lightweight materials; yet, evaluating the material's reliability under multi-axis stress states is still challenging owing to their anisotropic nature. This paper investigates the fatigue failures of short carbon-fiber reinforced polyamide-6 (PA6-CF) and polypropylene (PP-CF) by analyzing the anisotropic behavior induced by the fiber orientation. The static and fatigue experiment and numerical analysis results of a one-way coupled injection molding structure have been obtained to develop the fatigue life prediction methodology. The maximum deviation between the experimental and calculated tensile results is 3.16%, indicating the accuracy of the numerical analysis model. The obtained data were utilized to develop the semi-empirical model based on the energy function, consisting of stress, strain, and triaxiality terms. Fiber breakage and matrix cracking occurred simultaneously during the fatigue fracture of PA6-CF. The PP-CF fiber was pulled out after matrix cracking due to weak interfacial bonding between the matrix and fiber. The reliability of the proposed model has been confirmed with high correlation coefficients of 98.1% and 97.9% for PA6-CF and PP-CF, respectively. In addition, the prediction percentage errors of the verification set for each material were 38.6% and 14.5%, respectively. Although the results of the verification specimen collected directly from the cross-member were included, the percentage error of PA6-CF was still relatively low at 38.6%. In conclusion, the developed model can predict the fatigue life of CFRPs, considering anisotropy and multi-axial stress states.
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