Failure load prediction of adhesively bonded GFRP composite joints using artificial neural networks
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DOI 10.1007/s12206-020-1021-7
Journal of Mechanical Science and Technology 34 (11) 2020 Original Article DOI 10.1007/s12206-020-1021-7 Keywords: · Artificial neural network · Bonding joints · Composite materials · Failure load
Failure load prediction of adhesively bonded GFRP composite joints using artificial neural networks Bahadır Birecikli1, Ömer Ali Karaman1, Selahattin Bariş Çelebi1 and Aydın Turgut2 1
Correspondence to: Bahadır Birecikli [email protected]
Citation: Birecikli, B., Karaman, O. A., Çelebi, S. B., Turgut, A. (2020). Failure load prediction of adhesively bonded GFRP composite joints using artificial neural networks. Journal of Mechanical Science and Technology 34 (11) (2020) 4631~4640. http://doi.org/10.1007/s12206-020-1021-7
Received May 11th, 2020 Revised
July 24th, 2020
2
Vocational School of Technical Sciences, Batman University, 72060 Batman, Turkey, Department of Mechanical Engineering, Bingol University, 12000 Bingol, Turkey
Abstract There are different process parameters of bonding joints in the literature. The main objective of the paper was to investigate the effects of bonding angle, composite lay-up sequences and adherend thickness on failure load of adhesively bonded joints under tensile load. For this aim, the joint has four types of the bonding angles 30°, 45°, 60° and 75°. Composite materials have three different lay-up sequences and various thicknesses. The bonding angle, adherend thickness and composite lay-up sequences lead to an increase of the failure load. Moreover, artificial neural network that utilized Levenberg-Marquardt algorithm model was used to predict failure load of bonding joints. Mean square error was put into account to evaluate productivity of ANN estimation model. Experimental results have been consistent with the predicted results obtained with ANN for training, validation and testing data set at a rate of 0.998, 0.997 and 0.998 respectively.
Accepted August 12th, 2020 † Recommended by Editor Chongdu Cho
© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2020
1. Introduction Joint strength is important for aerospace, aviation and automotive industries [1]. Therefore, researchers require increasing joint strength. Bonding geometry, adhesive area and sample thickness have significant effect on the joint strength [2]. To improve the joint strength, one of the leading methods is increasing failure load. ANNs (artificial neural network) are robust computing technique that are widely used in solving many complex and poorly defined problem. Lately, studying artificial neural networks for anticipating has caused remarkable advance in engineering areas [3, 4]. Tosun and Çalık [5] figured out that failure load in single lap bonding joints was exposed to axial tensile load. Various bonding length and bonding area in single lap joint were used to find failure load. Levenberg-Marquardt model was applied to estimate relationship between failure load and input data. The mean error was found 0.997 % and
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