Creator:
Chiba, Ryoichi ; Kishida, Takuya ; Seki, Ryoto ; Sato, Seiya
Contributor:
Title:
Group publication title:
Subject and Keywords:
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.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Date:
Resource Type:
Format:
DOI:
Pages:
Source:
IJAME, volume 29, number 4 (2024)