Optimal filler content for cotton fiber/PP composite based on mechanical properties using artificial neural network

Abstract

In this paper, a machine learning-based approach has been proposed to integrate artificial intelligence during the designing of fiber-reinforced polymeric composites. With the help of the proposed approach, an artificial neural network (ANN) model has been developed to achieve the targeted filler content for cotton fiber/polypropylene composite while satisfying the required targeted properties. Previously obtained experimental data sets were trained on the TensorFlow backend using Keras library in Python, followed by hyperparameter tuning and k-fold cross-validation method for acquiring a better performing model to predict the amount of targeted filler content. The developed approach proved to be very efficient and reduced the time and effort of the material characterization for numerous samples, and it will help materials designers to design their future experiments effectively. The developed approach in this paper can be extended for other composite materials if the necessary experimental data are available to train the ANN model.