Shahrood University of Technology Journal of Solid and Fluid Mechanics 2251-9475 8 1 2018 03 21 An Experimental Study and Statistical Modelling on Compressive Properties of Epoxy/ Graphene/ Hydroxyapatite Nanocomposites An Experimental Study and Statistical Modelling on Compressive Properties of Epoxy/ Graphene/ Hydroxyapatite Nanocomposites 59 70 1196 10.22044/jsfm.2018.5873.2414 FA S. Fooladpanjeh A. Dadrasi Member of Board in Islamic Azad University- Shahrood Branch A. Alavi Gharebagh Journal Article 2017 06 11 In this article, an experimental and numerical study on compression strength, energy and elongation at break of epoxy based nanocomposites reinforced by graphene oxide and hydroxyapatite has been done. Graphene oxide and hydroxyapatite were used up to 0.5 & 7 wt.%, respectively. Filler's weight fractions that used as design parameters have been achieved by design of experiment through central composite design method. Weight fractions of fillers have been considered as input parameters for modeling by RSM, ANN & regression tree method. Experimental results show the addition of nanoparticles increase compression strength. Also, modeling results show that average error of ANN has the lowest average error. Optimization has been done by genetic algorithm method and the results show that the optimum value of compression strength was 23.95 Mpa in 7 wt.% of HA and 0.289 wt.% of GO. The optimum value of energy has been reported 33.3 J in 0.239 wt.% GO and in absence of HA nanoparticles. Also, the optimum value of elongation at break is in 0.224 wt.% GO and absence of HA that is equal to 23.98 percent. In this article, an experimental and numerical study on compression strength, energy and elongation at break of epoxy based nanocomposites reinforced by graphene oxide and hydroxyapatite has been done. Graphene oxide and hydroxyapatite were used up to 0.5 & 7 wt.%, respectively. Filler's weight fractions that used as design parameters have been achieved by design of experiment through central composite design method. Weight fractions of fillers have been considered as input parameters for modeling by RSM, ANN & regression tree method. Experimental results show the addition of nanoparticles increase compression strength. Also, modeling results show that average error of ANN has the lowest average error. Optimization has been done by genetic algorithm method and the results show that the optimum value of compression strength was 23.95 Mpa in 7 wt.% of HA and 0.289 wt.% of GO. The optimum value of energy has been reported 33.3 J in 0.239 wt.% GO and in absence of HA nanoparticles. Also, the optimum value of elongation at break is in 0.224 wt.% GO and absence of HA that is equal to 23.98 percent. https://jsfm.shahroodut.ac.ir/article_1196_c76b1ab5df275dc1d4b8ff2d7bd03c56.pdf