• A machine learning-based approach for fiber-reinforced Polyvinyl Chloride composites design.
• An artificial neural network to predict the load-displacement curves for composite materials.
• A better performing model to predict the behavioral pattern of varying cotton fiber filler content.
• Reduce time and effort of the material designers for intelligent product design.
• Approach is extendible for other composite materials, if required data are available.
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.
Details can be found in the following published journal: https://doi.org/10.1016/j.compstruct.2020.112885
Keywords: Machine learning, Artificial neural network, Fiber-reinforced polymer, Intelligent product design, Cotton fiber/PVC composite