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RISS 인기검색어
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- 저자 : Aydın Turgut (검색결과 2 건)
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Hamit Adin,Aydın Turgut 대한기계학회 2012 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.26 No.11
In this study, the tensile strength and failure loads of the inverse Z joints were analyzed both experimentally and numerically by using two adhesives with different properties under a tensile load. Vinylester Atlac 580 and Flexo Tix were used as adhesives and the joints were prepared with two different composite materials. Initially, the mechanical properties of the adhesives were specified using bulk specimens. Then, the stress analyses were performed using three-dimensional finite element method (3-D FEM) via Ansys (V.10.0.1). The experimental results were compared with the numerical results and they were found quite reasonable. According to the test results, it can be seen that when the adherend thickness is increased, the stress increases as well. The most appropriate value of the adherend thickness is identified as t = 5 mm. Furthermore, it was observed that the lowest failure load was obtained at t = 3 mm the thickness for each specimen.
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Failure load prediction of adhesively bonded GFRP composite joints using artificial neural networks
Bahadır Birecikli,Ömer Ali Karaman,Selahattin Bariş Çelebi,Aydın Turgut 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.11
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.
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