Prediction of Buckling Behaviour of Composite Plate Element Using Artificial Neural Networks
Katarzyna Falkowicz, Monika Kulisz
Abstract
This article presents the use of Artificial Neural Networks (ANNs) to analysis of the composite plate elements with cut-outs which can work as a spring element. The analysis were based on results from numerical approach. ANNs models have been developed utilizing the obtained numerical data to predict the composite plate’s flexural-torsional form of buckling as natural form for different cut-outs and angels configurations. The ANNs models were trained and tested using a large dataset, and their accuracy is evaluated using various statistical measures. The developed ANNs models demonstrated high accuracy in predicting the critical force and buckling form of thin-walled plates with different cut-out and fiber angels configurations under compression. The combination of numerical analyses with ANNs models provides a practical and efficient solution for evaluating the stability behaviour of composite plates with cut-outs, which can be useful for design optimization and structural monitoring in engineering applications.