A neuro-fuzzy based prediction modeling and mechanical characterization of multiwalled-carbon nanotubes filled Luffa cylindrica hybrid core and isotropic skin-based sandwich plate
Dhaneshwar Prasad Sahu, Jagesh Kumar Prusty, Sukesh Chandra Mohanty
Abstract
The study focuses on mechanical characterization and adaptive neuro-fuzzy inference system (ANFIS)-based prediction modeling of MWCNT-filled Luffa cylindrica hybrid core-based sandwich plates. Experiments determine natural frequencies using modal impact hammer tests with a Fast Fourier Transform (FFT) analyzer. Elastic properties from tensile tests utilized for numerical simulations of natural frequencies in ABAQUS, showing high consistency with experimental results. SEM analysis characterizes the fiber-matrix bond in the hybrid core. ANFIS modeling establishes input–output relationships for unseen data sets. Comparisons of numerical simulations with ANFIS predictions demonstrate an accuracy within a 5% margin of error, providing insights into dynamic behavior and potential applications.