Article 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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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

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(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.

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Keyword: powder bed fusion laser melting, Bayesian optimization, discrete element method, parameter identification, consolidation, powder spreading

Date published: 2025-11-21

Publisher: MDPI AG

Journal:

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

Funding:

  • Acquisition, Technology, and Logistics Agency JPJ004596

Manuscript type: Publisher's version (Version of record)

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First published URL: https://doi.org/10.3390/jmmp9120383

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Updated at: 2025-12-17 11:34:14 +0900

Published on MDR: 2025-12-19 14:11:46 +0900

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