@misc{Chiba_Ryoichi_Optimisation,
 author={Chiba, Ryoichi and Kishida, Takuya and Seki, Ryoto and Sato, Seiya},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 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.},
 abstract={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.},
 title={Optimisation of material composition in functionally graded plates using a structure-tuned deep neural network},
 type={artykuł},
 keywords={neural networks, optimal design, functionally graded material, thermal stresses, material design, multi-layered material},
}