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Licensed Unlicensed Requires Authentication Published by De Gruyter June 30, 2021

Performance evaluation of artificial neural networks for identification of failure modes in composite plates

Serkan Balli and Faruk Sen
From the journal Materials Testing

Abstract

The aim of this work is to identify failure modes of double pinned sandwich composite plates by using artificial neural networks learning algorithms and then analyze their accuracies for identification. Mechanically pinned specimens with two serial pins/bolts for sandwich composite plates were used for recognition of failure modes which were obtained in previous experimental studies. In addition, the empirical data of the preceding work was determined with various geometric parameters for various applied preload moments. In this study, these geometric parameters and fastened/bolted joint forms were used for training by artificial neural networks. Consequently, ten different backpropagation training algorithms of artificial neural network were applied for classification by using one hundred data values containing three geometrical parameters. According to obtained results, it was seen that the Levenberg-Marquardt backpropagation training algorithm was the most successful algorithm with 93 % accuracy rate and it was appropriate for modeling of this problem. Additionally, performances of all backpropagation training algorithms were discussed taking into account accuracy and error ratios.


Associate Prof. Dr. Serkan Balli Department of Information Systems Engineering Technology Faculty, Muğla Sıtkı Koçman University 48000 Kötekli-Muğla, Turkey

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Published Online: 2021-06-30
Published in Print: 2021-06-30

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