Increased recycling of PVC has become a requirement in industrial and scientific research level. Several studies will be realized. To confirm and check the recycled materials performance, it will be important to go through numerical modeling, which consists not only in validating the results of the experiments, but also in predicting what happens when the material is loaded. PVC material is more used in different fields, that ultimately means, after service life, an increase in waste requiring a high recycling rate. This article presents an approach validating the aging model as well as a numerical analysis predicting the mechanical properties of PVC after aging. The analysis samples (rigid and flexible) PVC which are subject to two types of accelerated aging, allows to obtain an aging model. Numerical modeling of PVC after aging is carried out using the finite element method and has been able to confirm the results obtained experimentally. Predicting the mechanical properties of rigid PVC after aging loaded with coconut and cow horn fibers after a first recycling is made by the finite element method, Mori-Tanaka and Double inclusion models. The obtained results have showed an improvement in the mechanical characteristics of the PVC studied using this natural bio-loading with these two fibers which respect the environment and have a lower cost and more lightness.
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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.