Date of Award

2025

Degree Type

Open Access Dissertation

Degree Name

Engineering and Industrial Applied Mathematics Joint PhD with California State University Long Beach, PhD

Program

Institute of Mathematical Sciences

Advisor/Supervisor/Committee Chair

Ehsan Barjasteh

Dissertation or Thesis Committee Member

Sara Moghtadernejad

Dissertation or Thesis Committee Member

Qidi Peng

Dissertation or Thesis Committee Member

Marina Chugunova

Terms of Use & License Information

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Rights Information

© 2025 Poom Narongdej

Keywords

3D printing, Advanced composites, Lattice structures, Machine learning, Mechanical properties, Nanocomposites

Subject Categories

Engineering Science and Materials | Mathematics

Abstract

Advanced composites have gained significant attention across various industries, including aerospace, automotive, clean energy, and healthcare, owing to their exceptional mechanical properties and versatility. Fiber-reinforced polymer (FRP) composites, particularly those reinforced with carbon fibers, are extensively used as structural materials in spacecraft, aircraft, high-performance vehicles, and wind turbines due to their high strength-to-weight ratios, stiffness, durability, and tailorable mechanical characteristics. In healthcare, the advent of additive manufacturing (3D printing) has expanded the utility of advanced composites, enabling precise customization of components to meet patient-specific needs while offering design flexibility and ease of fabrication. Despite these advantages, several challenges hinder the broader adoption of advanced composites. Critical issues include enhancing the delamination resistance and electrical conductivity of FRP composites, as well as improving the mechanical performance of 3D-printed materials while reducing weight and material waste. This research seeks to address these challenges through innovative solutions: (1) reducing carbon fiber-reinforced polymer (CFRP) delamination and enhancing electrical conductivity by incorporating polyamide (PA) and carbon non-woven veils; (2) improving the mechanical properties of digital light processing (DLP) 3D-printed materials via the addition of graphite nanoparticles; and (3) applying a novel, machine learning-aided analysis and printing method to reduce structural weight and material usage without compromising necessary mechanical integrity. The anticipated outcomes of this study aim to advance the design, performance, and application of advanced composites across these critical sectors, addressing current limitations and unlocking new possibilities for innovation.

ISBN

9798288854002

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