Graduation Semester and Year
Spring 2026
Language
English
Document Type
Thesis
Degree Name
Master of Science in Aerospace Engineering
Department
Mechanical and Aerospace Engineering
First Advisor
Paul Davidson
Second Advisor
Shiyao Lin
Third Advisor
Xin Liu
Abstract
Composite materials are widely used as structural panels in aerospace, automotive, and civil engineering applications, where buckling is often a critical failure mode. This thesis focuses on the analysis and design of composite laminates that maximize buckling performance under prescribed stiffness and thickness constraints.
The first part of the study investigates the buckling behavior of auxetic laminates, which exhibit a negative Poisson's ratio. While previous studies suggest that auxetic laminates can achieve higher critical buckling loads than non-auxetic laminates under simply supported boundary conditions with lateral restraint, the influence of other boundary conditions and plate aspect ratios has not been explored in detail. This work systematically examines the effects of different boundary conditions and aspect ratios, showing that the buckling advantage of auxetic laminates depends strongly on the lateral restraint provided by the unloaded edges, as well as on the plate aspect ratio.
The second part of the thesis addresses the identification of optimal stacking sequences for laminates with given thickness and stiffness requirements. A combined framework based on lamination parameters, machine learning models, and optimization techniques is developed to predict laminate configurations that yield high critical buckling loads. The proposed inverse design methodology is validated using finite element analysis, and the predicted buckling loads are found to be in good agreement with the numerical results. Overall, the study demonstrates that lamination parameters combined with machine learning provide an efficient approach for the stiffness-constrained design of buckling-optimized composite laminates.
Keywords
Auxetic Laminates, Buckling, Finite Element Method (FEM), Lamination Parameters, Machine Learning, Surrogate model, Stacking Sequence Design, Inverse design optimization
Disciplines
Structural Engineering | Structural Materials | Structures and Materials
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Maria Tamil Selvan, Hans Bendon, "Buckling analysis of auxetic composite laminates and optimal design using lamination parameters and machine learning" (2026). Mechanical and Aerospace Engineering Theses. 1.
https://mavmatrix.uta.edu/mechaerospace_theses2/1
Included in
Structural Engineering Commons, Structural Materials Commons, Structures and Materials Commons