ANN-based random first-ply failure analyses of laminated composite plates
ANN-based random first-ply failure analyses of laminated composite plates
This paper presents the random first-ply failure analyses of laminated composite plates by using an artificial neural network (ANN)-based surrogate model. In general, materials and geometric uncertainties are unavoidable in such structures due to their inherent anisotropy and randomness in system configuration. To map such variabilities, stochastic analysis corroborates the fact of inevitable edge towards the quantification of uncertainties. In the present study, the finite element formulation is derived based on the consideration of eight-noded elements wherein each node consists of five degrees of freedom (DOF). The five failure criteria namely, maximum stress theory, maximum strain theory, Tsai-Hill (energy-based criterion) theory, Tsai-Wu (interaction tensor polynomial) theory and Tsai-Hill’s Hoffman failure criteria are considered in the present study. The input parameters include the ply orientation angle, assembly of ply, number of layers, ply thickness and degree of orthotropy, while the first-ply failure loads for five criteria representing output quantity of interest. The deterministic results are validated with past experimental results. The results obtained from the ANN-based surrogate model are observed to attain fitment with the results obtained by Monte Carlo Simulation (MCS). The statistical results are presented for both deterministic, as well as stochastic domain.
Artificial Neural Network (ANN), First-ply failure, Laminated composite: Monte Carlo Simulation (MCS), Uncertainty quantification
131-142
Kushari, Subrata
be270505-67ef-4a98-9cdb-54026b8f0588
Chakraborty, A.
be13dce2-c2fe-46ed-98d0-19a004a27a55
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Maity, S. R.
b948351d-4550-4588-82a7-f2ba60940419
Dey, S.
ad19fb29-0675-43ef-85a6-69aedc394525
2021
Kushari, Subrata
be270505-67ef-4a98-9cdb-54026b8f0588
Chakraborty, A.
be13dce2-c2fe-46ed-98d0-19a004a27a55
Mukhopadhyay, T.
2ae18ab0-7477-40ac-ae22-76face7be475
Maity, S. R.
b948351d-4550-4588-82a7-f2ba60940419
Dey, S.
ad19fb29-0675-43ef-85a6-69aedc394525
Kushari, Subrata, Chakraborty, A., Mukhopadhyay, T., Maity, S. R. and Dey, S.
(2021)
ANN-based random first-ply failure analyses of laminated composite plates.
Saha, Sandip Kumar and Mukherjee, Mousumi
(eds.)
In Recent Advances in Computational Mechanics and Simulations - Volume-I: Materials to Structures.
vol. 103,
Springer Singapore.
.
(doi:10.1007/978-981-15-8138-0_11).
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Conference or Workshop Item
(Paper)
Abstract
This paper presents the random first-ply failure analyses of laminated composite plates by using an artificial neural network (ANN)-based surrogate model. In general, materials and geometric uncertainties are unavoidable in such structures due to their inherent anisotropy and randomness in system configuration. To map such variabilities, stochastic analysis corroborates the fact of inevitable edge towards the quantification of uncertainties. In the present study, the finite element formulation is derived based on the consideration of eight-noded elements wherein each node consists of five degrees of freedom (DOF). The five failure criteria namely, maximum stress theory, maximum strain theory, Tsai-Hill (energy-based criterion) theory, Tsai-Wu (interaction tensor polynomial) theory and Tsai-Hill’s Hoffman failure criteria are considered in the present study. The input parameters include the ply orientation angle, assembly of ply, number of layers, ply thickness and degree of orthotropy, while the first-ply failure loads for five criteria representing output quantity of interest. The deterministic results are validated with past experimental results. The results obtained from the ANN-based surrogate model are observed to attain fitment with the results obtained by Monte Carlo Simulation (MCS). The statistical results are presented for both deterministic, as well as stochastic domain.
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Published date: 2021
Additional Information:
Funding Information:
Acknowledgments The authors would like to acknowledge the Aeronautics Research and Development Board (AR&DB), Government of India (Project Sanction no.: ARDB/01/105885/M/I), for the financial support for the present research work.
Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Venue - Dates:
7th International Congress on Computational Mechanics and Simulation, 2019, , Mandi, India, 2019-12-11 - 2019-12-13
Keywords:
Artificial Neural Network (ANN), First-ply failure, Laminated composite: Monte Carlo Simulation (MCS), Uncertainty quantification
Identifiers
Local EPrints ID: 483945
URI: http://eprints.soton.ac.uk/id/eprint/483945
ISSN: 2366-2557
PURE UUID: e3b8ca98-7dd8-4742-a7a4-e40ff8dc3160
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Date deposited: 07 Nov 2023 18:34
Last modified: 18 Mar 2024 04:10
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Contributors
Author:
Subrata Kushari
Author:
A. Chakraborty
Author:
T. Mukhopadhyay
Author:
S. R. Maity
Author:
S. Dey
Editor:
Sandip Kumar Saha
Editor:
Mousumi Mukherjee
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