期刊
MATERIALS & DESIGN
卷 210, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2021.110056
关键词
Architectured ceramics; Interlocked building block; Machine learning; Finite element analysis; Thermal performance
资金
- National Research Council Canada through Security materials Technology and New Beginnings Initiative - Ideation Fund
- FRQNT (Fonds Nature et technologies) post-doctoral award
- Natural Sciences and Engineering Research Council of Canada through NSERC Discovery Grant [RGPIN-2016-0471]
- Canada Research Chairs program in Multifucntional Metamaterials
The use of machine learning in designing architectured ceramics can improve efficiency and performance, resulting in increased frictional energy dissipation, reduced sliding distance, lowered strain energy, higher safety factor, and delayed structural failure.
Topologically interlocked architectures can transform brittle ceramics into tougher materials, while making the material design procedure a cumbersome task since modeling the whole architectural design space is not efficient and, to a degree, is not viable. We propose an approach to design architectured ceramics using machine learning (ML), trained by finite element analysis data and together with a self-learning algorithm, to discover high-performance architectured ceramics in thermomechanical environments. First, topologically interlocked panels are parametrically generated. Then, a limited number of designed architectured ceramics subjected to a thermal load is studied. Finally, the multilinear perceptron is employed to train the ML model in order to predict the thermomechanical performance of architectured panels with varied interlocking angles and number of blocks. The developed feed-forward artificial neural network framework can boost the architectured ceramic design efficiency and open up new avenues for controllability of the functionality for various high-temperature applications. This study demonstrates that the architectured ceramic panels with the ML-assisted engineered patterns show improvement up to 30% in frictional energy dissipation and 7% in the sliding distance of the tiles and 80% reduction in the strain energy, leading to a higher safety factor and the structural failure delay compared to the plain ceramics. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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