Bio composites are a new category of materials using natural based components in their constituents. The study and simulation of the behavior of these innovative materials occupies an important place in the field of scientific research. Discovering and using new methods has always been the goal of researchers. In recent years, artificial intelligence has been very successful and is used in several fields. it represents a big part of today’s industrial revolution. Smart solutions are more and more favored over conventional solutions as they give more precise results in a short time. We can find them in different sectors, such as banking, commerce, transport and industry, especially in materials science.The intersection of the artificial intelligence with materials engineering, gives extraordinary results. This smart method was able to boost the discovery of new materials, and to solve the most complex problems encountered when determining the mechanical properties of bio composites. What characterizes theEco-composites is their light in weight, their sustainable development, and that they are environmentally friendly. However, the determination of their mechanical properties is not obvious. Certainly, solutions based on homogenization methods or even on the finite element method have given good results, but the complexity of the microstructure of these materials limits the determination of their characteristics. In our paper, we hilight the use of Deep Learning that is an artificial intelligence machine learning method that relies on neural networks to predict the mechanical behavior of a polypropylen bioloaded by the natural fibers.

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.

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