Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates


The present study deals with the development of a prediction model to investigate the impact of temperature and moisture on the vibration response of a skew laminated composite sandwich (LCS) plate using the artificial neural network (ANN) technique. Firstly, a finite element model is generated to incorporate the hygro-elastic and thermo-elastic characteristics of the LCS plate using first-order shear deformation theory (FSDT). Graphite-epoxy composite laminates are used as the face sheets, and DYAD606 viscoelastic material is used as the core material. Non-linear strain-displacement relations are used to generate the initial stiffness matrix in order to represent the stiffness generated from the uniformly varying temperature and moisture concentrations. The mechanical stiffness matrix is derived using linear strain-displacement associations. Then the results obtained from the numerical model are used to train the ANN. About 11,520 data points were collected from the numerical analysis and were used to train the network using the Levenberg–Marquardt algorithm. The developed ANN model is used to study the influence of various process parameters on the frequency response of the system, and the outcomes are compared with the results obtained from the numerical model. Several numerical examples are presented and conferred to comprehend the influence of temperature and moisture on the LCS plates.

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Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences
Additional Information: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( Funding: This research was funded by the Deanship of Scientific Research at King Khalid University, Saudi Arabia, grant number RGP.1/132/42. The authors would also like to thank the Science and Engineering Research Board (DST-SERB), Govt. of India, grant number EEQ/2017/000744 for the non-funded support.
Uncontrolled Keywords: Artificial neural network,Effect of temperature and moisture,Finite element analysis,Sandwich plates,Shear deformation theory,Skew angle,Materials Science(all),Condensed Matter Physics
Publication ISSN: 1996-1944
Last Modified: 14 May 2024 07:25
Date Deposited: 11 Jun 2021 08:45
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Related URLs: https://www.mdp ... 1944/14/12/3170 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-06-09
Accepted Date: 2021-05-29
Authors: Kallannavar, Vinayak
Kattimani, Subhaschandra
Soudagar, Manzoore Elahi M.
Mujtaba, M. A.
Alshahrani, Saad
Imran, Muhammad (ORCID Profile 0000-0002-3057-1301)



Version: Published Version

License: Creative Commons Attribution

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