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- W4366990600 abstract "In this paper, a coupled finite element method (FEM) and supervised machine learning (SML) scheme has been proposed and implemented for predicting the static deflection in laminated composite plate for subsequent design and synthesis. To do away with the time, cost and need of repeatability associated with experimental data, a well-established nonlinear higher-order FE model using full geometrical nonlinearity in Green-Lagrange sense has been used to compute the central deflection values of laminated composite plates. Desired responses are acquired via a self-developed MATLAB computer code as per Taguchi's experimental design considering the lay-up scheme, aspect ratio, thickness ratio and the support conditions of the laminated composite plate structure as the controllable factors under five different uniformly distributed load parameters. This numerically prepared dataset has been utilized to find the best performing regression model using ANN and Supervised Regression Methodologies for accurate prediction of deflection based on current data as input. The results suggest the Random Forest Regression model provide more satisfactory prediction of nonlinear static responses. The proposed methodology can be incorporated as a procedural step to accelerate the design and successive manufacturing of smart material structure." @default.
- W4366990600 created "2023-04-27" @default.
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- W4366990600 date "2023-04-24" @default.
- W4366990600 modified "2023-10-18" @default.
- W4366990600 title "Implementation of FEM and Machine Learning Algorithms in the Design and Manufacturing of Laminated Composite Plate" @default.
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- W4366990600 doi "https://doi.org/10.1002/9781119836780.ch9" @default.
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