Optimization of Dynamic and Buckling Behavior of Thin-Walled Composite Cylinder, Supported by Nature-Inspired Agorithms

Multi-Objective Optimization of Thin-Walled Composite Axisymmetric Structures Using Neural Surrogate Models and Genetic Algorithms

Composite shells find diverse applications across industries due to their high strength-to-weight ratio and tailored properties. Optimizing parameters such as matrix-reinforcement ratio and orientation of the reinforcement is crucial for achieving the desired performance metrics. Stochastic optimization, specifically genetic algorithms, offer solutions, yet their computational intensity hinders widespread use. Surrogate models, employing neural networks, emerge as efficient alternatives by approximating objective functions and bypassing costly computations. This study investigates surrogate models in multi-objective optimization of composite shells. It incorporates deep neural networks to approximate relationships between input parameters and key metrics, enabling exploration of design possibilities. Incorporating mode shape identification enhances accuracy, especially in multi-criteria optimization. Employing network ensembles strengthens reliability by mitigating model weaknesses. Efficiency analysis assesses required computations, managing the trade-off between cost and accuracy. Considering complex input parameters and comparing against the Monte Carlo approach further demonstrates the methodology's efficacy. This work showcases the successful integration of network ensembles employed as surrogate models and mode shape identification, enhancing multi-objective optimization in engineering applications. The approach's efficiency in handling intricate designs and enhancing accuracy has broad implications for optimization methodologies.

Modeling of Curvilinear Steel Rod Structures Based on Minimal Surfaces

The article deals with shaping effective curvilinear steel rod roof structures using genetic algorithms by implementing them for the analysis of various case studies in order to find new and efficient structures with positive characteristics. The structures considered in this article are created on the basis of the Enneper surface and minimal surfaces stretched on four arcs. On the Enneper surface, a single layer grid is used, while on the other surfaces, two-layer ones. The Enneper form structure with four supports and the division into an even number of parts along the perimeter of the covered place proved to be the most efficient, and the research showed that small modifications of the initial base surface in order to adapt the structure to the roof function did not significantly affect its effectiveness. However, the analysis and comparison of single and double-shell rod structures based on minimal surfaces stretched on four arcs have shown that a single-shell structure is much more effective than a double one. The paper considers the theoretical aspects of shaping effective structures, taking their masses as the optimization criterion. The optimization helped to choose the best solutions due to structures’ shapes and topologies. However, the obtained, optimized results can find practical applications after conducting physical tests.

Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks

This paper presents a numerical study of the feasibility of using vibration mode shapes to identify material degradation in composite structures. The considered structure is a multilayer composite cylinder, while the material degradation zone is, for simplicity, considered a square section of the lateral surface of the cylinder. The material degradation zone size and location along the cylinder axis are identified using a deep learning approach (convolutional neural networks, CNNs, are applied) on the basis of previously identified vibration mode shapes. The different numbers and combinations of identified mode shapes used to assess the damaged zone size and location were analyzed in detail. The final selection of mode shapes considered in the identification procedure yielded high accuracy in the identification of the degradation zone.

Design of Circular Composite Cylinders for Optimal Natural Frequencies

This study concerns optimizing the eigenfrequencies of circular cylindrical laminates. The stiffness properties are described by lamination parameters to avoid potential solution dependency on the initial assumptions of the laminate configurations. In the lamination parameter plane, novel response contours are obtained for the first and second natural frequencies as well as their difference. The influence of cylinder length, radius, thickness, and boundary conditions on the responses is investigated. The lamination parameters yielding the maximum response values are determined, and the first two mode shapes are shown for the optimum points. The results demonstrate that the maximum fundamental frequency points of the laminated cylinders mostly lie at the inner lamination parameter domain, unlike the singly curved composite panels. In addition, the second eigenfrequency shows a nonconvex response surface containing multiple local maxima for several cases. Moreover, the frequency difference contours appear as highly irregular, which is unconventional for free vibration responses.

Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks

The aim of the following paper is to discuss a newly developed approach for the identification of vibration mode shapes of multilayer composite structures. To overcome the limitations of the approaches based on image analysis (two-dimensional structures, high spatial resolution of mode shapes description), convolutional neural networks (CNNs) are applied to create a three-dimensional mode shapes identification algorithm with a significantly reduced number of mode shape vector coordinates. The CNN-based procedure is accurate, effective, and robust to noisy input data. The appearance of local damage is not an obstacle. The change of the material and the occurrence of local material degradation do not affect the accuracy of the method. Moreover, the application of the proposed identification method allows identifying the material degradation occurrence.