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ORIGINAL PAPER
Optimisation of material composition in functionally graded plates using a structure-tuned deep neural network
1
Mechanical Engineering, Sanyo-Onoda City University, Japan
2
Mechanical Systems Engineering, National Institute of Technology, Asahikawa College, Japan
Submission date: 2024-04-25
Final revision date: 2024-08-02
Acceptance date: 2024-08-13
Online publication date: 2024-12-12
Publication date: 2024-12-12
Corresponding author
Ryoichi Chiba
Mechanical Engineering, Sanyo-Onoda City University, 1-1-1 Daigakudori, 7560884, Sanyo-Onoda, Japan
Mechanical Engineering, Sanyo-Onoda City University, 1-1-1 Daigakudori, 7560884, Sanyo-Onoda, Japan
International Journal of Applied Mechanics and Engineering 2024;29(4):78-95
KEYWORDS
TOPICS
ABSTRACT
This study presents a neural network (NN)-based approach for optimising material composition in multi-layered functionally graded (FG) plates to minimise steady-state thermal stress. The focus is on the metal-ceramic composition across the thickness of heat-resistant FG plates, with the volume fractions of the ceramic constituent in each layer treated as design variables. A fully-connected NN, configured with an open-source Bayesian optimisation framework, is employed to predict the maximum in-plane thermal stress for various combinations of design variables. The optimal distribution of material composition is determined by applying a backpropagation algorithm to the NN. Numerical computations on ten- and twelve-layered FG plates demonstrate the usefulness of this approach in designing FG materials. NNs trained with 8000 samples enable the successful acquisition of valid optimal solutions within a practical timeframe.
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