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

作     者：Fooladpanjeh, Sasan Dadrasi, Ali Gharahbagh, Abdorreza Alavi Parvaneh, Vali 

作者机构：Islamic Azad Univ Shahrood Branch Dept Mech Engn Shahrood Iran Islamic Azad Univ Shahrood Branch Dept Elect & Comp Engn Shahrood Iran 

出 版 物：《PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE》 (机械工程师学会会报；C辑：机械工程学杂志)

年 卷 期：2021年第235卷第23期

页      面：7169-7179页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 

主　　题：Hydroxyapatite graphene oxide fuzzy neural network gene expression programming particle swarm optimization 

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.