論文 Bayesian Optimization-Based Parameter Identification for Discrete Element Method Simulation of Consolidation and Its Application to Powder Spreading Analysis

Jun Katagiri ORCID (National Institute for Materials Science) ; Masahiro Kusano SAMURAI ORCID (National Institute for Materials Science) ; Makoto Watanabe SAMURAI ORCID (National Institute for Materials Science)

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引用
Jun Katagiri, Masahiro Kusano, Makoto Watanabe. Bayesian Optimization-Based Parameter Identification for Discrete Element Method Simulation of Consolidation and Its Application to Powder Spreading Analysis. Journal of Manufacturing and Materials Processing. 2025, 9 (12), 383. https://doi.org/10.3390/jmmp9120383

説明:

(abstract)

This study develops a Bayesian optimization framework to calibrate two discrete element method (DEM) parameters—the cohesion-related surface energy coefficient (k) and the rolling resistance coefficient (µr)—based on experimental void ratio data obtained from powder consolidation tests.

権利情報:

キーワード: powder bed fusion laser melting, Bayesian optimization, discrete element method, parameter identification, consolidation, powder spreading

刊行年月日: 2025-11-21

出版者: MDPI AG

掲載誌:

  • Journal of Manufacturing and Materials Processing (ISSN: 25044494) vol. 9 issue. 12 383

研究助成金:

  • Acquisition, Technology, and Logistics Agency JPJ004596

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.3390/jmmp9120383

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更新時刻: 2025-12-17 11:34:14 +0900

MDRでの公開時刻: 2025-12-19 14:11:46 +0900

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