Prediction of Buckling Behaviour of Composite Plate Element Using Artificial Neural Networks

• Autorzy: Falkowicz Katarzyna , Kulisz Monika

Identyfikatory

DOI

10.12913/22998624/177399

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

artificial neural network; numerical analysis; thin-walled structures; buckling

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no 1
• Strony: 231--243
• Opis fizyczny: Bibliogr. 50 poz., fig. tab.

Twórcy

autor

Falkowicz Katarzyna

• Faculty of Mechanical Engineering, Department of Machine Design and Mechatronics, Lublin University of Technology

• https://orcid.org/0000-0002-3007-1462

autor

Kulisz Monika

• Faculty of Management, Department of Organisation of Enterprise, Lublin University of Technology

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).