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Mechanics of Composite Materials
Published in: Mechanics of Composite Materials 3/2023

28-06-2023

A Comparative Study of the Estimated Bending Strength of Sandwich Panels by an Artificial Neural Network and an Adaptive Neuro-Fuzzy Inference System

Authors: M. Nazerian, B. S. Deghatkar, E. Vatankhah

Published in: Mechanics of Composite Materials | Issue 3/2023

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Abstract

The effects of cattail/poplar straw weight ratio, sandwich panel thickness, and the number of kraft paper layers at the core of sandwich panels on the modulus of rupture (MOR) of the panels were predicted using an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The prediction accuracy of the MOR based on experimental data, in terms of the mean absolute percentage error, was smaller than 1.4 and 1.1%, with coefficients of determination 0.94 and 0.995 for the ANN and ANFIS models, respectively. These models can easily predict the bending strength of lignocellulosic-based sandwich panels with a high precision in the manufacturing process.
previous article Dynamic Stability of a Composite Sandwich Shell with Longitudinal Ribs and an Internal Cylinder Loaded by an External Pressure and a Time-Varying Axial Force
next article Size-Dependent Thermal Backbone Curves for Nanocomposite Reddy Microbeams Reinforced with Graphene Platelets and Resting on Elastic Foundation

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Metadata
Title
A Comparative Study of the Estimated Bending Strength of Sandwich Panels by an Artificial Neural Network and an Adaptive Neuro-Fuzzy Inference System
Authors
M. Nazerian
B. S. Deghatkar
E. Vatankhah
Publication date
28-06-2023
Publisher
Springer US
Published in
Mechanics of Composite Materials / Issue 3/2023
Print ISSN: 0191-5665
Electronic ISSN: 1573-8922
DOI
https://doi.org/10.1007/s11029-023-10118-6

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