Failure load prediction of adhesively bonded GFRP composite joints using artificial neural networks

• Published in: Journal of mechanical science and technology 2020, 34(11), , pp.4631-4640
• Main Authors: Birecikli, Bahadır, Karaman, Ömer Ali, Çelebi, Selahattin Bariş, Turgut, Aydın
• Format: Article
• Language: English
• Subjects: Adhesive bonding; Adhesive joints; Algorithms; Artificial neural networks; Bonded joints; Composite materials; Control; Dynamical Systems; Engineering; Failure load; Glass fiber reinforced plastics; Industrial and Production Engineering; Lay-up; Mechanical Engineering; Neural networks; Original Article; Process parameters; Tensile stress; Thickness; Vibration; 기계공학
• ISSN: 1738-494X; 1976-3824
• DOI: 10.1007/s12206-020-1021-7

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.