Home / Regular Issue / JST Vol. 29 (4) Oct. 2021 / JST-2794-2021

 

Optimisation Design of Functionally Graded Sandwich Plate with Porous Metal Core for Buckling Characterisations

Emad Kadum Njim, Sadeq Hussein Bakhy and Muhannad Al-Waily

Pertanika Journal of Science & Technology, Volume 29, Issue 4, October 2021

DOI: https://doi.org/10.47836/pjst.29.4.47

Keywords: Classical plate theory, critical buckling load, DOE, FEA, FGPMs, mathematical model, optimisation, RMS

Published on: 29 October 2021

This study presents the optimum operating parameters and geometrical of the functionally graded sandwich plate with porous materials (FGPMs), widely used in aircraft structures subjected to uniaxial critical buckling load. This process is developed design optimisation parameters by employing Multi-Objective Genetic Algorithm (MOGA) techniques. According to a simple power law, the assumption of varying material characteristics of the porous FG core through the plate thickness is considered. In addition, to evaluate the linear buckling behaviour, a new mathematical model based on the classical plate theory (CPT) is proposed. The impact of different design parameters on the performance of the functionally graded structure is studied. Then, finite element modelling is used to validate the results of the analytical solution. Finally, the optimisation method includes both design of experiments (DOE) and response surface methodology (RSM), which are used to find out the critical buckling load of the FG sandwich plate with porous metal core bonded with two homogenous skins using suitable adhesion. The mandatory constraints are the maximum critical buckling and maximum total deformation. In this work, 100 design points are considered to determine the total deformation load multiplier, maximum deformation, and equivalent stress of sandwich plate with graded materials and even distribution of porosities. The buckling analyses of the FGPM sandwich plate subjected to the compression loading are presented by conducting an experimental program. The results show good convergence between suggested analytical and FEA simulation with an average error percentage of no more than 2 %.

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