Treffer: Prediction of the buckling mode of cylindrical composite shells with imperfections using FEM-based deep learning approach.

This study introduces a new method for predicting buckling modes in cylindrical composite shells using a deep learning approach based on finite element method (FEM). The study used carbon fiber woven prepreg material and collected 520 data sets through an Abaqus-Python program. A deep neural network was trained using FEM results to accurately predict the buckling mode of cylindrical composite shells with imperfections. The study employed a generative adversarial network and the pix2pix deep learning algorithm in the prediction process. The deep learning model achieved a similar level of prediction accuracy as traditional FEM, but was much more efficient. The study also utilized logistic regression to examine the relationship between input variables and buckling modes. The research shows the potential of the FEM-based deep learning approach to improve the efficiency of buckling mode prediction in cylindrical composite shells, which is critical for their design and safety. [ABSTRACT FROM AUTHOR]

Copyright of Advanced Composite Materials is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Volltext ist im Gastzugang nicht verfügbar. Login für vollen Zugriff.