Abstract
The determination of the properties of bio composites is the key subject of current research. The mechanical characteristics of these new materials depend on several factors, we can note the characteristics of the constituents, the process followed during the manufacture, etc. Several solutions have been made available to determine these properties such as experience tests, the methods of homogenization and the finite element method. Currently, thanks to artificial intelligence, smart solutions have been able to give better results, particularly in the prediction of the mechanical properties of materials. In this article, we will use artificial neural networks to predict the Young’s modulus of polypropylene loaded at 15% with horn fibers. Using Mori–Tanaka’s model, which is a form of the homogenization method, we will generate a dataset to feed our feed forward back propagation and demonstrate that this bio composite is gaining in terms of elasticity with a very good value of coefficient regression, 099, and with a best validation of performance, 0.034737 obtained at epoch 11.
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Laabid, Z., Moumen, A., Lakhdar, A., Mansouri, K. (2022). Toward the Prediction of the Elasticity of Bio Loaded Polypropylene Using Artificial Neural Networks. In: Saeed, F., Al-Hadhrami, T., Mohammed, E., Al-Sarem, M. (eds) Advances on Smart and Soft Computing. Advances in Intelligent Systems and Computing, vol 1399. Springer, Singapore. https://doi.org/10.1007/978-981-16-5559-3_5
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DOI: https://doi.org/10.1007/978-981-16-5559-3_5
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