Preprint Article Version 1 This version is not peer-reviewed

The Influence of Composite Laminate Stacking Sequence on Failure Load of Bonding Joints Using Experimental and Artificial Neural Networks Methods

Version 1 : Received: 24 December 2018 / Approved: 25 December 2018 / Online: 25 December 2018 (09:32:08 CET)

How to cite: Birecikli, B.; Karaman, O.A.; Celebi, S.B.; Turgut, A. The Influence of Composite Laminate Stacking Sequence on Failure Load of Bonding Joints Using Experimental and Artificial Neural Networks Methods. Preprints 2018, 2018120302 (doi: 10.20944/preprints201812.0302.v1). Birecikli, B.; Karaman, O.A.; Celebi, S.B.; Turgut, A. The Influence of Composite Laminate Stacking Sequence on Failure Load of Bonding Joints Using Experimental and Artificial Neural Networks Methods. Preprints 2018, 2018120302 (doi: 10.20944/preprints201812.0302.v1).

Abstract

The objective of this article was to forecast the ultimate failure load laminate stacking sequence combination on bonding joints which are exposed to tensile strength by using artificial neural networks. We have glass fiber composite materials with three different sequence combinations ([0°/90°], [±45°], [0°/90°/±45°]). Various adherend thicknesses and also ductile type adhesive was used in the experiment. The bonding geometry is a single lap and has four types of overlap angles 30°, 45°, 60°, 75° respectively. The experimental results demonstrate that composite laminate stacking sequence profoundly affects the bonding joints of failure load. Taking experimental results into account, Levenberg–Marquardt learning algorithm model was used by preferring a three layer forward on ANN so as to discipline network. In order to procure a precise ANN tool, an integrate methodology of experimental method has been used. The outcomes are used to ensure the experimental data’s to the ANN. The method of ANN permits surveying much adequately the probabilities of composite laminate stacking sequence combination using the prevalent ones which are [0°/90°], [±45°] and [0°/90°/±45°]. Testing data and training results were quite well 0.998, 0.997 and 0.998 in turn. Consequences acquired can be used by engineers who are interested in the composite material design to enhance failure load.

Subject Areas

experimental tests; composite laminates; tensile strength; artificial neural networks

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