Loading...
Synthesis, evaluation and machine learning integration of fire-retardant fiber composites for aviation industries
Murad, Md. Shafinur
Murad, Md. Shafinur
Citations
Altmetric:
Authors
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2025-05
Type
Dissertation
Genre
Keywords
Subjects (LCSH)
Electronic dissertation
Citation
Abstract
Fiber-reinforced polymeric composites have been widely employed in a variety of industrial applications due to their superior properties. However, these composites often have limited tolerance for fire, lightning, and bird strike damage. One of the unique ways to address this problem is to develop a flame-retardant fiber-reinforced composite using modified resins and metallic copper (Cu) film surface coatings. In the first part of this report, a thorough investigative review was conducted to better understand the various flame-retardant additives properties, as well as the synergistic effects of additives on mechanical, thermal, chemical, and structural integrity. In the second part of this research, the development process for manufacturing flame-retardant composites by synthesizing Loctite resin via 9,10-dihydro-9-oxo-10 phosphaphenanthrene-10-oxide (DOPO) inclusion and with metallic surface coatings, as well as the testing and characterization of the mechanical and thermal properties of the resulting structures, are covered. Composite panels were manufactured using a standard hand wet layup process, with flame retardant properties determined before and after resin modification and surface metal film coatings. The developed composite passed UL-94 flame testing with V0 rating and achieved high flexural, tensile, and shear strength. Microscopic images, C-scan, and SEM results confirmed good bonding between matrix and fiber. Smoke density and toxicity test results were encouraging, demonstrating nearly no smoke formation and very little release of harmful gases because of the improved fire-shielding and heat-dissipation capabilities. The research has further explored the use of machine learning algorithms as a novel tool for predicting composite mechanical properties. This study may open new opportunities for enhancing the characteristics of fiber composites for various manufacturing industries under diverse environmental conditions.
Table of Contents
Description
Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Mechanical Engineering
Publisher
Wichita State University
Journal
Book Title
Series
Digital Collection
Finding Aid URL
Use and Reproduction
© Copyright 2025 by Md. Shafinur Murad
All Rights Reserved