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

Vinayak Kallannavar; Subhaschandra Kattimani; Manzoore Elahi M. Soudagar; M. A. Mujtaba; Saad Alshahrani; Muhammad Imran

doi:10.3390/ma14123170

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

Vinayak Kallannavar^*, Subhaschandra Kattimani, Manzoore Elahi M. Soudagar, M. A. Mujtaba, Saad Alshahrani^*, Muhammad Imran

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Article number	3170
Journal	Materials
Volume	14
Issue number	12
DOIs	https://doi.org/10.3390/ma14123170
Publication status	Published - 9 Jun 2021

Bibliographical note

© 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 (https://creativecommons.org/licenses/by/4.0/).

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.

Keywords

Artificial neural network
Effect of temperature and moisture
Finite element analysis
Sandwich plates
Shear deformation theory
Skew angle

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Cite this

@article{378828ac36d7472f840371653ae747a2,

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

abstract = "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.",

keywords = "Artificial neural network, Effect of temperature and moisture, Finite element analysis, Sandwich plates, Shear deformation theory, Skew angle",

author = "Vinayak Kallannavar and Subhaschandra Kattimani and Soudagar, {Manzoore Elahi M.} and Mujtaba, {M. A.} and Saad Alshahrani and Muhammad Imran",

note = "{\textcopyright} 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 (https://creativecommons.org/licenses/by/4.0/). 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.",

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month = jun,

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doi = "10.3390/ma14123170",

language = "English",

volume = "14",

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Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates. / Kallannavar, Vinayak; Kattimani, Subhaschandra; Soudagar, Manzoore Elahi M. et al.
In: Materials, Vol. 14, No. 12, 3170, 09.06.2021.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

AU - Kallannavar, Vinayak

AU - Kattimani, Subhaschandra

AU - Soudagar, Manzoore Elahi M.

AU - Mujtaba, M. A.

AU - Alshahrani, Saad

AU - Imran, Muhammad

N1 - © 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 (https://creativecommons.org/licenses/by/4.0/). 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.

PY - 2021/6/9

Y1 - 2021/6/9

N2 - 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.

AB - 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.

KW - Artificial neural network

KW - Effect of temperature and moisture

KW - Finite element analysis

KW - Sandwich plates

KW - Shear deformation theory

KW - Skew angle

UR - https://www.mdpi.com/1996-1944/14/12/3170

UR - http://www.scopus.com/inward/record.url?scp=85108348591&partnerID=8YFLogxK

U2 - 10.3390/ma14123170

DO - 10.3390/ma14123170

M3 - Article

SN - 1996-1944

VL - 14

JO - Materials

JF - Materials

IS - 12

M1 - 3170

ER -

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

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Keywords

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