Prediction of Buckling Behaviour of Composite Plate Element Using Artificial Neural Networks
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Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
Faculty of Management, Department of Organisation of Enterprise, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland
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Katarzyna Falkowicz   

Politechnika Lubelska
Adv. Sci. Technol. Res. J. 2024; 18(1):231-243
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.
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