Digital Library of Zielona Góra contains 9 272 digital objects
Object
Title: Optimisation of material composition in functionally graded plates using a structure-tuned deep neural network
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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
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Pages:
Source:
IJAME, volume 29, number 4 (2024)
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License:
License CC BY-NC-ND 4.0:
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Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Repository > Faculties > Faculty of Mechanical Engineering
- Repository > Types of work > Articles
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mechanics and Engineering (IJAME)
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mechanics and Engineering (IJAME) > International Journal of Applied Mechanics and Engineering (IJAME) (2024)
Last modified:
Dec 16, 2024
In our library since:
Dec 16, 2024
Number of object content hits:
130
All available object's versions:
https://zbc.uz.zgora.pl/repozytorium/publication/91610
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| Edition name | Date |
|---|---|
| Optimisation of material composition in functionally graded plates using a structure-tuned deep neural network | Dec 16, 2024 |
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