Durability and long-term performance of fiber-reinforced composite structures exposed to extreme environments
type: CreativeWork
description: Fiber-reinforced composite (FRC) materials have gained significant traction in aircraft, space, defense, marine, and energy applications owing to their outstanding mechanical properties, lightweight nature, high strength, and corrosion resistance. However, ensuring their reliability under diverse environmental conditions remains a critical challenge. This research investigated failure mechanisms of FRC materials under extreme mechanical, thermal, and chemical exposures. The objectives were to: examine composite behavior under various mechanical testing conditions; evaluate aviation fluid effects on mechanical and viscoelastic performance; investigate thermal stability at elevated temperatures; and predict composite performance using machine learning methods. Aerospace-grade fiber composites were fabricated and subjected to static and dynamic loadings. Carbon fiber composites exhibited superior mechanical properties with tensile, flexural, and shear strengths of 488, 576, and 65 MPa, respectively, and thermal stability up to 850°C. Fiber-matrix cracking and delamination were the primary failure modes observed. Thermal aging studies revealed that composites maintained resilience until exceeding the glass transition temperature, where interlaminar shear stress sharply declined. Characterization revealed discoloration, reduced wettability, and interlaminar cracking. Aviation fluid exposure testing demonstrated that methyl ethyl ketone (MEK) posed the greatest risk, inducing the highest fluid uptake (5.5% in carbon fiber, 5% in glass fiber) and severe interlaminar shear strength losses (15% and 22%, respectively). Carbon fiber composites exhibited superior chemical resistance compared to glass fiber counterparts due to stronger interfacial bonding. Machine learning algorithms successfully predicted flexural strengths of FRPCs. Extra trees regressor achieved the best performance (R² = 0.94, RMSE = 47.34), demonstrating ML's potential to reduce reliance on costly experimen…
abstract: Fiber-reinforced composite (FRC) materials have gained significant traction in aircraft, space, defense, marine, and energy applications owing to their outstanding mechanical properties, lightweight nature, high strength, and corrosion resistance. However, ensuring their reliability under diverse environmental conditions remains a critical challenge. This research investigated failure mechanisms of FRC materials under extreme mechanical, thermal, and chemical exposures. The objectives were to: examine composite behavior under various mechanical testing conditions; evaluate aviation fluid effects on mechanical and viscoelastic performance; investigate thermal stability at elevated temperatures; and predict composite performance using machine learning methods. Aerospace-grade fiber composites were fabricated and subjected to static and dynamic loadings. Carbon fiber composites exhibited superior mechanical properties with tensile, flexural, and shear strengths of 488, 576, and 65 MPa, respectively, and thermal stability up to 850°C. Fiber-matrix cracking and delamination were the primary failure modes observed. Thermal aging studies revealed that composites maintained resilience until exceeding the glass transition temperature, where interlaminar shear stress sharply declined. Characterization revealed discoloration, reduced wettability, and interlaminar cracking. Aviation fluid exposure testing demonstrated that methyl ethyl ketone (MEK) posed the greatest risk, inducing the highest fluid uptake (5.5% in carbon fiber, 5% in glass fiber) and severe interlaminar shear strength losses (15% and 22%, respectively). Carbon fiber composites exhibited superior chemical resistance compared to glass fiber counterparts due to stronger interfacial bonding. Machine learning algorithms successfully predicted flexural strengths of FRPCs. Extra trees regressor achieved the best performance (R² = 0.94, RMSE = 47.34), demonstrating ML's potential to reduce reliance on costly experimen…
author: Hamzat, Abdulhammed Kanmi
date published: 2026-05
date modified: 2026-07-24T06:05:48Z
provider: Wichita State University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Wichita State University