Inverse Analysis of DEM Input Parameters Using Powder Properties by Machine Learning Technique
Author:
Affiliation:
1. Graduate School of Engineering, Osaka Metropolitan University
2. (Currently) Materials Research & Development Laboratory, Japan Fine Ceramics Center
Funder
Hosokawa Powder Technology Foundation
Publisher
Link
https://www.jstage.jst.go.jp/article/hptf/31/0/31_HPTF21124/_pdf
Reference14 articles.
1. Boikov A.V., Savelev R.V., Payor V.A., Vasileva N.V., DEM calibration approach: orthogonal experiment, Journal of Physics: Conference Series, 1210 (2019) 012025. https://doi.org/10.1088/1742-6596/1210/1/012025
2. Coetzee C.J., Review: calibration of the discrete element method, Powder Technology, 310 (2017) 104–142. https://doi.org/10.1016/j.powtec.2017.01.015
3. Cundall P.A., Strack O.D.L., A discrete numerical model for granular assemblies, Géotechnique, 29 (1979) 47–65. https://doi.org/10.1680/geot.1979.29.1.47
4. Elskamp F., Kruggel-Emden H., Hennig M. Teipel U., A strategy to determine DEM parameters for spherical and non-spherical particles, Granular Matter, 19 (2017) 46. https://doi.org/10.1007/s10035-017-0710-0
5. Ghadiri M., Pasha M., Nan W., Hare C., Vivacqua V, Zafar U., Nezamabadi S., Lopez A., Pasha M., Nadimi S., Cohesive powder flow: trends and challenges in characterisation and analysis, KONA Powder and Particle Journal, 37 (2020) 3–18. https://doi.org/10.14356/kona.2020018
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