Autor:

Chiba, Ryoichi ; Kishida, Takuya ; Seki, Ryoto ; Sato, Seiya

Współtwórca:

Jurczak, Paweł - red.

Tytuł:

Optimisation of material composition in functionally graded plates using a structure-tuned deep neural network

Tytuł publikacji grupowej:

IJAME, volume 29 (2024)

Temat i słowa kluczowe:

neural networks ; optimal design ; functionally graded material ; thermal stresses ; material design ; multi-layered material

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2024

Typ zasobu:

artykuł

Format:

application/pdf

DOI:

10.59441/ijame/192278

Strony:

78-95

Źródło:

IJAME, volume 29, number 4 (2024)

Jezyk:

eng

Licencja:

CC 4.0

Licencja CC BY-NC-ND 4.0:

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Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego