Predicting the effect of cooling rate on the mechanical properties of glass fiber–polypropylene composites using artificial neural networks

Abstract

Properties of thermoplastic-based composites are affected by their processing conditions, and understanding their behavior under these different conditions is of most importance. The current study aims to predict the static tensile behavior of unidirectional glass fiber–polypropylene composite materials processed under different cooling rates using artificial neural networks (ANNs). Stress–strain relations for the material processed under various cooling rates were predicted using ANN. For all the cases investigated, the modulus of elasticity was predicted with a minimum accuracy of 97%, while the ultimate strain was predicted, in most cases, with a minimum accuracy of 90%. These predictions indicate that ANN can be successfully used to predict the mechanical properties of unidirectional composites manufactured under different cooling rates. This method allows users to predict the behavior of the material under cooling rate conditions for which no experimental data are available.