Multi-objective optimization of fiber reinforced laminated hybrid composite plates using particle swarm algorithm
Multi-objective optimization of fiber reinforced laminated hybrid composite plates using particle swarm algorithm
Dosyalar
Tarih
2024-07-02
Yazarlar
Yegit, Orhan Nuri
Süreli Yayın başlığı
Süreli Yayın ISSN
Cilt Başlığı
Yayınevi
Graduate School
Özet
This study investigates the multi-objective optimization of layered hybrid composite structures under axial compressive loads using the particle swarm optimization (PSO) approach. A simply supported composite plate consisting of 64 layers was subjected to constant axial compressive loads in two directions. The classical plate theory (Kirchoff plate theory) was employed to perform the analytical calculation of the plate's critical buckling load. Additionally, since the plate is a layered structure, the necessary formulas were derived using the classical lamination theory. Parameters such as the plate's edge lengths, layer thickness, layer material, total number of layers, magnitude and ratio of the compressive loads in both directions were derived from a previously studied problem in the literature. Thus, the calculations were validated by comparing them with the results obtained in the literature. Extensive studies on hybrid composites have demonstrated their superior design flexibility and mechanical properties compared to non-hybrid composites. In this purpose, a hybrid plate was created by employing carbon/epoxy and glass/epoxy materials. The positioning of plies with different material properties was carefully considered during the hybridization process and taking into account its impact on the plate's bending rigidity. Therefore, carbon/epoxy layers exhibiting higher bending rigidity were placed farther from the symmetry axis, whereas less rigid glass/epoxy layers were positioned closer to the symmetry axis. By employing this strategy, the elements of the bending rigidity matrix, which directly impact the critical buckling coefficient, were effectively maximized. The cost function was determined by utilizing previous works in the literature. Instead of using the actual material prices, the cost ratio of the two materials was calculated and incorporated into the equation. Population optimization algorithms are powerful tools employed for solving complex problems. Different types of population optimization algorithms such as genetic algorithm, ant colony optimization (ACO) algorithm, differential evaluation (DE) algorithm. In this study, particle swarm optimization (PSO) was utilized. The primary motivation for selecting this algorithm arises from the demonstrated evidence from previous studies indicating its superior convergence speed and performance compared to other stochastic algorithms. The objective functions were defined as maximizing the buckling load, minimizing the cost function, and maximizing the difference between the first two natural frequency values. The weighted sum method and the $\epsilon$ constraint method were employed to solve the multi-objective optimization problem. Since the objective functions were conflicting, Pareto optimal solutions were obtained. The optimization was repeated with different load and aspect ratios to investigate the effects of these parameters on the results.
Açıklama
Thesis (M.Sc.) -- İstanbul Technical University, Graduate School, 2024
Anahtar kelimeler
Hybrid structures,
Hibrit yapılar,
Composite plates,
Kompozit levhalar,
Composite materials,
Kompozit malzemeler