Investigation of Tensile Behavior of Carbon Nanotube/Coir Fiber/Fly Ash Reinforced Epoxy Polymer Matrix Composite

被引:7
|
作者
Venkatachalam, Gopalan [1 ,6 ]
Hemanth, Velivela [2 ]
Logesh, Molanganuru [2 ]
Piyush, Arora [2 ]
Kumar, Mahalingam Siva [3 ]
Pragasam, Vignesh [4 ]
Loganathan, Thozhuvur Govindaraman [5 ]
机构
[1] Vellore Inst Technol, Ctr Innovat & Prod Dev, Chennai, India
[2] Vellore Inst Technol, Sch Mech Engn, Chennai, India
[3] Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T, Dept Mech Engn, Chennai, India
[4] Kings Engn Coll, Dept Mech Engn, Chennai, Tamil Nadu, India
[5] RMK Coll Engn & Technol, Dept Mech Engn, Chennai, Tamil Nadu, India
[6] Vellore Inst Technol, Ctr Innovat & Prod Dev, Chennai Campus, Chennai 600127, Tamil Nadu, India
关键词
Central Composite Design (CCD); Multiple Linear Regression (MLR); Artificial Neural Networks (ANN); fillers; epoxy composite; tensile properties; FLY-ASH;
D O I
10.1080/15440478.2022.2148151
中图分类号
TB3 [工程材料学]; TS1 [纺织工业、染整工业];
学科分类号
0805 ; 080502 ; 0821 ;
摘要
Fiber-reinforced composite materials are lightweight and can withstand heavy loading conditions. Reinforcement augments the strength of the composite material, which is assessed by its elastic modulus. An attempt is made to reinforce epoxy with Coir Fiber, Carbon Nanotube (CNT) and Fly-ash. Central Composite Design (CCD), a Response Surface Methodology (RSM) tool, which is a Design of Experiment (DOE) technique, is used to fabricate the experimental samples to study their tensile behavior. Analysis of variance (ANOVA) is employed to investigate the effect of reinforcement percentage of CNT, coir-fiber, and fly-ash on tensile behavior of composite. The ANOVA results follow the trend of the experimental values with a deviation of less than 10% in yield strength, tensile strength, and Young's modulus. Two models (Artificial Neural Network and Multiple Linear Regression Model) originated resting on the regression equation to speculate the elastic modulus for various reinforcement parameters using the experimental data. The main objective is to optimize reinforcement parameters using both the models having a maximum elastic modulus of 2.602 GPa and 2.682 GPa, respectively, which is achieved by the teaching learning-based optimization technique. Furthermore, confirmation experiments validate the optimization process with an error of less than 4%.
引用
收藏
页数:15
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