Fabrication and modelling of the macro-mechanical properties of cross-ply laminated fibre-reinforced polymer composites using artificial neural network |
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Authors: | Shahzad Maqsood Khan Samander Ali Malik Nafisa Gull Sidra Saleemi Atif Islam Muhammad Taqi Zahid Butt |
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Affiliation: | 1. Department of Polymer Engineering and Technology, University of the Punjab, Lahore 54590, Pakistanshahzad.pet.ceet@pu.edu.pk dr.shahzadmkhan@hotmail.com nafisagull@gmail.com;2. Institute of Textile Machinery and High Performance Material Technology, Technische Universit?t Dresden, Dresden 01062, Germany;3. Department of Textile Engineering, Mehran University of Engineering &4. Technology, Jamshoro 76062, Pakistan;5. Department of Polymer Engineering and Technology, University of the Punjab, Lahore 54590, Pakistan;6. Department of Textile Engineering &7. Technology, University of the Punjab, Lahore 54590, Pakistan;8. College of Engineering and Emerging Technologies, University of the Punjab, Lahore, 54590, Pakistan |
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Abstract: | The mechanical behaviour of fibre-reinforced polymer composites (FRPCs) is considered very complex due to many factors such as composition, material type, manufacturing process and end user applications. This article presents the mechanical properties and artificial neural network (ANN) modelling results of cross-ply laminated FRPCs. Twenty composite samples were fabricated by varying the number of layers of carbon fibre and glass fibre as reinforcement and polyphenylene sulphide and high-density polyethylene as matrix. Mechanical properties were measured in terms of flexural modulus, hardness, impact and transverse rupture strength. Multilayer feed-forward backpropagation ANN approach was used to predict the mechanical properties by using material type, composition and number of reinforcement and matrix layers as input variables. From 20 data patterns, 16 were used for network training and remaining 4 were used to test the models. Furthermore, trend analysis was also performed to understand the influence of inputs on developed models. It is evident from the ANN prediction results that there is good correlation between predicted and actual values within acceptable mean absolute error. The outcomes of this research will help to reduce cost and time by eliminating tedious composite property measurements and to fabricate tailored composites meeting application requirements. |
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Keywords: | fibre-reinforced polymer composites cross-ply laminate mechanical properties artificial neural network modelling |
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