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Article

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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Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal 575025, India
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Department of Mechanical Engineering, Glocal University, Delhi-Yamunotri Marg, Uttar Pradesh 247121, India
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Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
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Department of Mechanical Engineering, King Khalid University, Guraiger, Abha 62529, Saudi Arabia
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Department of Mechanical, Biomedical and Design Engineering, College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK
*
Authors to whom correspondence should be addressed.
Academic Editor: Nicholas Fantuzzi
Materials 2021, 14(12), 3170; https://doi.org/10.3390/ma14123170
Received: 30 April 2021 / Revised: 27 May 2021 / Accepted: 29 May 2021 / Published: 9 June 2021
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. View Full-Text
Keywords: artificial neural network; finite element analysis; shear deformation theory; skew angle; sandwich plates; effect of temperature and moisture artificial neural network; finite element analysis; shear deformation theory; skew angle; sandwich plates; effect of temperature and moisture
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MDPI and ACS Style

Kallannavar, V.; Kattimani, S.; Soudagar, M.E.M.; Mujtaba, M.A.; Alshahrani, S.; Imran, M. Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates. Materials 2021, 14, 3170. https://doi.org/10.3390/ma14123170

AMA Style

Kallannavar V, Kattimani S, Soudagar MEM, Mujtaba MA, Alshahrani S, Imran M. Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates. Materials. 2021; 14(12):3170. https://doi.org/10.3390/ma14123170

Chicago/Turabian Style

Kallannavar, Vinayak, Subhaschandra Kattimani, Manzoore E.M. Soudagar, M. A. Mujtaba, Saad Alshahrani, and Muhammad Imran. 2021. "Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates" Materials 14, no. 12: 3170. https://doi.org/10.3390/ma14123170

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