Predictive ANN models for varying filler content for cotton fiber/PVC composites based on experimental load displacement curves
Author | Kazi, M.-K. |
Author | Eljack, F. |
Author | Mahdi, E. |
Available date | 2023-09-10T17:35:33Z |
Publication Date | 2020 |
Publication Name | Composite Structures |
Resource | Scopus |
Abstract | In this paper, artificial neural network (ANN) models are developed to predict the load-displacement curves for better understanding the behavior of cotton fiber/polyvinyl chloride (PVC) composites. Series of experiments were undertaken in the laboratory for a varying percentage of composite fiber to characteristic loading. Based on those experimental data, the ANN models were trained and tested on the TensorFlow backend using Keras library in Python by implementing the back-propagation method. For better prediction and accuracy of the load-displacement curves, the grid search hyperparameter tuning method was used, followed by k-fold cross-validation. The developed approach proved to be very efficient and reduced the time and effort of the behavioral study for numerous samples, and it will help materials designers to design their future experiments effectively. A similar approach to predict load-displacement curves using ANN can be extended for any kind of composite material if the necessary experimental data are available. 2020 Elsevier Ltd |
Sponsor | This paper was made possible by NPRP grant No 10-0205-170347 from the Qatar National Research Fund (a member of Qatar Foundation). |
Language | en |
Publisher | Elsevier Ltd |
Subject | Artificial neural network Cotton fiber/PVC composite Fiber-reinforced polymer Intelligent product design Machine learning |
Type | Article |
Volume Number | 254 |
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Chemical Engineering [1141 items ]
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Mechanical & Industrial Engineering [1339 items ]