Prediction of the mechanical properties of Polypropylene reinforced with Snail Shell Powder with a Deep Neural Network Model and the Finite Element Method

Abstract

Neural networks have led to the evolution of the processing methodology of computational sciences. The problems like bio composites modeling and prediction are difficult to model with classical mathematical and statistical tools because of the data inherent noise. NN’s processing capability in the forecasting, recognition, modeling, system analysis and control can give fast characterization, modeling and prediction of bio composites properties, provided as long as datasets are available. Using Matlab®, a neural network model was evaluated to characterize the optimal properties of the ANS reinforced the Polypropylene. The feed forward multilayer model provided best results in comparison with the finite element method and the experimental tensile tests. The trained neural network is able to provide a best prediction of such bio composite based on natural particles having more advantages to the environment, economy and the sustainable development.

Numerical study of the speed’s response of the various intelligent models using the tansig, logsig and purelin activation functions in different layers of artificial neural network

“…Using 1,000 repeated epochs of training, the regression produced by our model is shown in the Figure 5. The regression coefficient of polypropylene [19] reinforced at 15% with a choosen bio load is 0.99 and that converges to 1 and signified that our model is efficient and performant [20]- [25]. hidden and output layers, we were able to build 9 models that we studied.…”

Thermogravimetric Analysis of Rigid PVC and Animal-Origin Bio-Composite: Experimental Study and Comparative Analysis

“…Furthermore, the additional contributions of Moumen et al pushed the boundaries of research by exploring the mechanical characteristics of the bio-composite using an artificial intelligence approach. The use of neural networks to predict the mechanical properties of the bio-composite by varying bioloading and PVC percentages demonstrates an innovative and multidisciplinary approach [21,22].…”