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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
