A Comparative Analysis of Buckling Pressure Prediction in Composite Cylindrical Shells Under External Loads Using Machine Learning

Composite materials are increasingly utilized in engineering due to their superior properties such as strength, flexibility, and corrosion resistance. However, accurately predicting the buckling pressure of composite cylindrical shells under external loads remains challenging due to the complexities introduced by various stacking methods. This study addressed this challenge by integrating advanced machine learning techniques with simulation-based data generation through finite element analysis (FEA). A comprehensive dataset comprising 1369 simulation results was generated using ANSYS ACP, focusing on cylindrical shells modeled with an 8-mm-thick filament winding technique and T700 material. The stacking angles ranged from −90 degrees to 90 degrees in 5-degree increments. Stacking configurations (inputs) and their corresponding buckling strength (outputs) were generated using ANSYS ACP. Machine learning models, including linear regression, elastic net, polynomial regression, random forest, support vector regression, XGBoost regression, and artificial neural networks, were implemented using Python 3.8 and Scikit-learn (version 0.24.2). A comparative analysis of these methods revealed their model performance, providing insights into the most effective approaches. Additionally, the accuracy of these models was then evaluated on previously unseen input data, allowing for a comparison of their out-of-sample accuracy. The results demonstrated that the random forest model and XGBoost regression achieved superior accuracy with minimal prediction errors. The study highlights the critical role of machine learning in predicting buckling pressure, which is essential for ensuring structural integrity and optimizing performance in marine engineering and other applications involving composite materials.