Enhancing mechanical performance of MWCNT filler with jute/kenaf/glass composite: a statistical optimization study using RSM and ANN
S. Jothi Arunachalam, R. Saravanan, T. Sathish, Abdullah A. Alarfaj, Jayant Giri, Ajay Kumar
Abstract
Fibre sequencing greatly affects bending and hardness of fibre-reinforced composites. Fiber-matrix bonding, orientation, and sequencing boost composite strength, especially when nanoparticles are added to improve mechanical properties. Lining fibres with loading direction optimises conditions. Fibre orientation, sequencing, and MWCNT addition affected the composite’s flexural strength and hardness in this study. Fibre orientation, sequencing, and nanoparticle incorporation were examined. The study examined how fibre properties affect quality using Response Surface Methodology (RSM) and Analysis of Variance (ANOVA). An Artificial Neural Network (ANN) examined several factors. Model-expected response values were strongly correlated. Fibre orientation is the main factor affecting composite flexural strength. All fibre factors affected flexural strength and hardness, but fibre orientation was most critical. Nanofillers stimulated fibre matrix aggregation, increasing strength. An artificial neural network predicted flexural strength and hardness 95% accurately. Experimental data, regression model, and ANN were less accurate than the Artificial Neural Network.