Finite element and generalized regression neural network modelling of multiple cracks growth under the influence of multiple crack parameters
Multidiscipline Modeling in Materials and Structures
ISSN: 1573-6105
Article publication date: 27 July 2023
Issue publication date: 10 August 2023
Abstract
Purpose
This study presents finite element (FE) and generalized regression neural network (GRNN) approaches for modeling multiple crack growth problems and predicting crack-growth directions under the influence of multiple crack parameters.
Design/methodology/approach
To determine the crack-growth direction in aluminum specimens, multiple crack parameters representing some degree of crack propagation complexity, including crack length, inclination angle, offset and distance, were examined. FE method models were developed for multiple crack growth simulations. To capture the complex relationships among multiple crack-growth variables, GRNN models were developed as nonlinear regression models. Six input variables and one output variable comprising 65 training and 20 test datasets were established.
Findings
The FE model could conveniently simulate the crack-growth directions. However, several multiple crack parameters could affect the simulation accuracy. The GRNN offers a reliable method for modeling the growth of multiple cracks. Using 76% of the total dataset, the NN model attained an R2 value of 0.985.
Research limitations/implications
The models are presented for static multiple crack growth problems. No material anisotropy is observed.
Practical implications
In practical crack-growth analyses, the NN approach provides significant benefits and savings.
Originality/value
The proposed GRNN model is simple to develop and accurate. Its performance was superior to that of other NN models. This model is also suitable for modeling multiple crack growths with arbitrary geometries. The proposed GRNN model demonstrates its prediction capability with a simpler learning process, thus producing efficient multiple crack growth predictions and assessments.
Keywords
Acknowledgements
The author is grateful to the reviewers for their comments and discussions, which further improved the quality of this paper.
Funding: This research work is supported by the grant provided by Kemendikbudristek RI/Institut Teknologi Sepuluh Nopember (ITS) Surabaya (No. 1205/PKS/ITS/2023), which is gratefully acknowledged.
Citation
Hidayat, M.I.P., Pramata, A.D. and Airlangga, P.P. (2023), "Finite element and generalized regression neural network modelling of multiple cracks growth under the influence of multiple crack parameters", Multidiscipline Modeling in Materials and Structures, Vol. 19 No. 5, pp. 1014-1041. https://doi.org/10.1108/MMMS-03-2023-0105
Publisher
:Emerald Publishing Limited
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