2020年7月
Acoustic emission analysis using Bayesian model selection for damage characterization in ceramic matrix composites
Journal of the European Ceramic Society
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- 巻
- 40
- 号
- 8
- 開始ページ
- 2791
- 終了ページ
- 2800
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1016/j.jeurceramsoc.2020.03.035
© 2020 Elsevier Ltd Acoustic emission (AE) during tensile testing of three-dimensional woven SiC/SiC composites was analyzed by a statistical modeling method based on a Bayesian approach to quantitatively evaluate the fracture process. Gaussian mixture models and Weibull mixture models were utilized as candidate models describing the AE time-series data. After fitting AE time-series data to these models with Markov Chain Monte Carlo (MCMC) methods, the model selection was conducted by stochastic complexity. Among the candidate models, the two-component Weibull mixture model was automatically selected. It was confirmed that the component distributions in the two-component Weibull mixture model were corresponding to the evolution of matrix cracking and fiber breakage, respectively. Since the proposed AE analysis method can determine the number of component distributions without the decision of researchers and inspectors, it is expected to be useful for an understanding of the fracture process in newly developed materials and the reliability assessment in service.
- リンク情報
- ID情報
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- DOI : 10.1016/j.jeurceramsoc.2020.03.035
- ISSN : 0955-2219
- eISSN : 1873-619X
- SCOPUS ID : 85082017210