Optimisation of material composition in functionally graded plates using a structure-tuned deep neural network - Chiba, Ryoichi; Kishida, Takuya; Seki, Ryoto; Sato, Seiya (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: neural networks; optimal design; functionally graded material; thermal stresses; material design; multi-layered material