• 文献标题:   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
  • 文献类型:   Article
  • 作  者:   FOOLADPANJEH S, DADRASI A, GHARAHBAGH AA, PARVANEH V
  • 作者关键词:   hydroxyapatite, graphene oxide, fuzzy neural network, gene expression programming, particle swarm optimization
  • 出版物名称:   PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART CJOURNAL OF MECHANICAL ENGINEERING SCIENCE
  • ISSN:   0954-4062 EI 2041-2983
  • 通讯作者地址:  
  • 被引频次:   3
  • DOI:   10.1177/09544062211019771 EA MAY 2021
  • 出版年:   2021

▎ 摘  要

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.