Quantifying uncertainty in structural responses of polymer sandwich composites: a comparative analysis of neural networks
Quantifying uncertainty in structural responses of polymer sandwich composites: a comparative analysis of neural networks
The manufacturing and fabrication of complex polymer sandwich composite plates involve various processes and parameters, and the lack of control over them causes uncertain system parameters. It is essential to consider randomness in varying parameters to analyse polymer sandwich composite plates. The present study portrays uncertainty quantification in structural responses (such as natural frequencies) of polymer sandwich composite plates using the surrogate model. The comparative study of artificial neural network (ANN) and polynomial neural network (PNN) for uncertain structural responses of the sandwich plate is presented. The proposed ANN as well as PNN algorithm is found to be convergent with intensive Monte Carlo simulation (MCS) for uncertain vibration responses. The predictability of PNN is observed to be more efficient than that of ANN. Typical material properties, skew angle, fibre orientation angle, number of laminate and core thickness are randomly varied to quantify the uncertainties. The use of both the surrogate models (PNN and ANN) results in a significant saving of computational time and cost compared to that of full-scale intensive finite element-based MCS approach.
Artificial neural network, Monte carlo simulation, Polymer sandwich plate, Polynomial neural network, Randomness
305-315
Kumar, R. R.
fbb581d9-1968-4788-87ce-36b6596a8392
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Pandey, K. M.
59e9b535-7819-4a7b-a472-021b3816b11c
Dey, S.
4d43ac00-9799-4452-80d7-8d278a7cbad4
2021
Kumar, R. R.
fbb581d9-1968-4788-87ce-36b6596a8392
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Pandey, K. M.
59e9b535-7819-4a7b-a472-021b3816b11c
Dey, S.
4d43ac00-9799-4452-80d7-8d278a7cbad4
Kumar, R. R., Mukhopadhyay, T., Pandey, K. M. and Dey, S.
(2021)
Quantifying uncertainty in structural responses of polymer sandwich composites: a comparative analysis of neural networks.
Adhikari, Sondipon, Dutta, Anjan and Choudhury, Satyabrata
(eds.)
In Advances in Structural Technologies - Select Proceedings of CoAST 2019.
vol. 81,
Springer Singapore.
.
(doi:10.1007/978-981-15-5235-9_23).
Record type:
Conference or Workshop Item
(Paper)
Abstract
The manufacturing and fabrication of complex polymer sandwich composite plates involve various processes and parameters, and the lack of control over them causes uncertain system parameters. It is essential to consider randomness in varying parameters to analyse polymer sandwich composite plates. The present study portrays uncertainty quantification in structural responses (such as natural frequencies) of polymer sandwich composite plates using the surrogate model. The comparative study of artificial neural network (ANN) and polynomial neural network (PNN) for uncertain structural responses of the sandwich plate is presented. The proposed ANN as well as PNN algorithm is found to be convergent with intensive Monte Carlo simulation (MCS) for uncertain vibration responses. The predictability of PNN is observed to be more efficient than that of ANN. Typical material properties, skew angle, fibre orientation angle, number of laminate and core thickness are randomly varied to quantify the uncertainties. The use of both the surrogate models (PNN and ANN) results in a significant saving of computational time and cost compared to that of full-scale intensive finite element-based MCS approach.
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More information
Published date: 2021
Additional Information:
Funding Information:
The first author would like to acknowledge the financial support received from MHRD, Government of India during this research work.
Funding Information:
Acknowledgements The first author would like to acknowledge the financial support received from MHRD, Government of India during this research work.
Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2021.
Venue - Dates:
National Conference on Advances in Structural Technology, CoAST 2019, , Silchar, India, 2019-02-01 - 2019-02-03
Keywords:
Artificial neural network, Monte carlo simulation, Polymer sandwich plate, Polynomial neural network, Randomness
Identifiers
Local EPrints ID: 483889
URI: http://eprints.soton.ac.uk/id/eprint/483889
ISSN: 2366-2557
PURE UUID: e0ce244b-e49b-4f68-bb3d-509870564918
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Date deposited: 07 Nov 2023 18:05
Last modified: 18 Mar 2024 04:10
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Contributors
Author:
R. R. Kumar
Author:
T. Mukhopadhyay
Author:
K. M. Pandey
Author:
S. Dey
Editor:
Sondipon Adhikari
Editor:
Anjan Dutta
Editor:
Satyabrata Choudhury
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