Experimental and artificial neural network approach for prediction of dynamic mechanical behavior of sisal/glass hybrid composites

Abstract

The dynamic mechanical behavior (storage modulus, loss modulus, and tan δ) of hybrid sisal/glass composites was investigated in the temperature range of 30–210 °C, for two different volume percentages of reinforcement along with the different ratios of sisal and glass fibers. Based on the experimental outcome, an artificial neural network (ANN) approach was used to predict the dynamic mechanical properties followed by a surface response methodology (SRM). The ANN analysis showed an excellent fit with the storage modulus, loss modulus, and tan δ experimental data. In addition, the fitted curves with the ANN approach were used to propose equations based on SRM. The simulation result has shown that the ANN is a potential mathematical tool for the structure–property correlation for polymer composites and may help researchers in the development and application of their data, reducing the need for long experimental campaigns.