論文 Data-driven approach for rapid prediction of strength scatter in brittle ceramics using deep learning and swarm optimization

Taiyo Maeda (Graduate School of Yokohama National University) ; Muhammad Aiman bin Musa (Graduate School of Yokohama National University) ; Toshio Osada SAMURAI ORCID (Research Center for Structural Materials/Materials Manufacturing Field/High-Reliability Heat-Resistant Materials Group, National Institute for Materials Science) ; Shingo Ozaki (Faculty of Engineering, Yokohama National University)

Data-driven approach for rapid prediction of strength scatter in brittle ceramics using deep learning and swarm optimization.pdf
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Taiyo Maeda, Muhammad Aiman bin Musa, Toshio Osada, Shingo Ozaki. Data-driven approach for rapid prediction of strength scatter in brittle ceramics using deep learning and swarm optimization. Science and Technology of Advanced Materials-Methods. 2026, 6 (1), . https://doi.org/10.1080/27660400.2026.2656050

説明:

(abstract)

The intrinsic brittleness and high defect sensitivity of ceramics result in significant strength variability, presenting substantial challenges for structural reliability assessments. Experimental characterization of strength scatter in ceramic components is both time-consuming and costly. Conventional physics-based (forward) analyses can model strength scatter based on microstructural data; these methods are computationally intensive. This study introduces a deep learning–based surrogate model that directly predicts the Weibull distribution parameters of ceramic bending strength from equivalent crack length distributions, achieving substantial reductions in computational cost without compromising predictive accuracy. Additionally, an inverse analysis framework is developed by integrating the surrogate model with intelligent swarm optimization, enabling the estimation of defect distributions from reference strength measurements. The proposed approach demonstrates high accuracy and efficiency, achieving over 200-fold speedup in forward analysis and 18,000-fold in inverse analysis. It facilitates a rapid and reliable evaluation of the relationship between defect distributions and strength scatter as characterized by Weibull parameters. This methodology provides a robust tool for accelerating the design and development of high-performance ceramic materials.

権利情報:

キーワード: Ceramics, Fracture statistics, Defect distribusion, Multilayer perceptron, Inverse analysis

刊行年月日: 2026-12-31

出版者: Informa UK Limited

掲載誌:

  • Science and Technology of Advanced Materials-Methods (ISSN: 27660400) vol. 6 issue. 1

研究助成金:

  • Japan Society for the Promotion of Science

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/27660400.2026.2656050

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更新時刻: 2026-04-22 16:01:04 +0900

MDRでの公開時刻: 2026-04-22 18:24:14 +0900

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