Polymeric nanocomposites have proven to be excellent candidate as biomaterials. However, materials and approaches used to improve the mechanical property of the polymer are still under scrutiny. In this study, improvement of mechanical property upon addition of nanotitanium oxide (n-TiO2), cellulose nanocrystal (CNC) and two different types of coupling agent was analyzed. Influence of the individual factors and its interaction with tensile strength was evaluated using analysis of variance. From the analyses of main effect and interaction effects, it could be concluded that n-TiO2and CNC have major influence on the improving mechanical properties. Moreover, the coupling agent and compatibilizing agent did not have considerable effect on the mechanical properties. The central composite design was used to evaluate the best combination of n-TiO2and CNC to be experimented. The responses were modeled and optimized using response surface methodology (RSM) and artificial neural network (ANN). The predicted data was in agreement with the experimental data. The modeling accuracy and efficiency is evaluated based on regression coefficient (R square value). Both the method had recommendable R square value. However, the R square value of the Artificial neural network (R2>95%) was higher than Response surface methodology (R2>70 %).
Mechanical property prediction of SPS processed GNP/PLA polymer nanocomposite using artificial neural network
