Fuzzy neural network and coupled gene expression programming/multivariate non-linear regression approach on mechanical features of hydroxyapatite/graphene oxide/epoxy: Empirical and optimization study

One way to enhance the mechanical properties of nanocomposites has been to use different fillers. In this study, ternary hybrid composites of graphene oxide/hydroxyapatite/epoxy resin were investigated. An experimental design was performed based on the central composite design (CCD). Epoxy resin was modified by incorporating different graphene oxide and hydroxyapatite weight from 0 to 0.5 wt.% and 0 to 7 wt.%, respectively. Experimental results showed that Young's modulus, yield strength and impact strength improved up to 25.64%, 5.95% and 100.05% compared to the neat epoxy resin, respectively. In addition, gene expression programming (GEP), multivariate non-linear regression (MNLR) and fuzzy neural network (FNN) methods were employed to determine the effects of nanoparticles on the mechanical properties. Based on the modelling results, optimization process was investigated by using particle swarm optimization (PSO). Finally, the fracture surface morphologies of the nanocomposites were analyzed by scanning electron microscopy.

Automated summary

The study investigates the enhancement of mechanical properties of nanocomposites through experiments and modeling methods, focusing on ternary hybrid composites of graphene oxide/hydroxyapatite/epoxy resin. Results demonstrate that incorporating different graphene oxide and hydroxyapatite significantly improves the mechanical properties of the material, resulting in enhancements in Young's modulus, yield strength, and impact strength.