Optimizing powder compaction for enhanced relative density: Insights from multi-particle finite element simulations and genetic algorithm

–

Optimizing powder compaction for enhanced relative density: Insights from multi-particle finite element simulations and genetic algorithm

GHORBANI-MENGHARI Hossein, KIM Hwi-Jun, CHOI Hyunjoo, CHA Pil-Ryung, KIM Ji Hoon

Abstract. In this study, multi-particle finite element simulations in powder compaction were performed to analyze the effects of the size of the representative volume element (RVE), the number of elements per particle, and particle size distribution. Simulation parameters were calibrated to accurately predict the relative density of compacts derived from two types of powders. The influence of RVE size across four mixtures was examined to obtain its relationship with relative density. The impacts of particle size distribution and element number per particle were studied. The results indicate a decline in relative density with increased element size. Moreover, a genetic algorithm is employed to determine the optimum mixture composition yielding the highest relative density at 1400 MPa.

Keywords
Powder Compaction, Element Number Per Particle, Particle Size Distribution, Multi-Particle Finite Element Modeling

Published online 4/24/2024, 8 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: GHORBANI-MENGHARI Hossein, KIM Hwi-Jun, CHOI Hyunjoo, CHA Pil-Ryung, KIM Ji Hoon, Optimizing powder compaction for enhanced relative density: Insights from multi-particle finite element simulations and genetic algorithm, Materials Research Proceedings, Vol. 41, pp 2554-2561, 2024

DOI: https://doi.org/10.21741/9781644903131-281

The article was published as article 281 of the book Material Forming

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

References
[1] V. Mazel, H. Diarra, P. Tchoreloff, Effect of friction between powder and tooling on the die-wall pressure evolution during tableting: Experimental and numerical results for flat and concave punches, Int. J. Pharma, 554 (2019) 116-124. https://doi.org/10.1016/j.ijpharm.2018.11.003
[2] W. Wang, H. Qi, P. Liu, Y. Zhao, H. Chang, Numerical simulation of densification of cu–al mixed metal powder during axial compaction, Int. J. Metals. 8 (2018) 537. https://doi.org/10.3390/met8070537
[3] R. Cabiscol, H. Shi, I. Wünsch, V. Magnanimo, J.H. Finke, S. Luding, A. Kwade, Effect of particle size on powder compaction and tablet strength using limestone, Int. J. Adv. Powder. Tech. 31 (2020) 1280-1289. https://doi.org/10.1016/j.apt.2019.12.033
[4] Y. Bai, G. Wagner, C.B. Williams, Effect of particle size distribution on powder packing and sintering in binder jetting additive manufacturing of metals, Int. J. Manuf. Sci. Eng. 139 (2017) 081019. https://doi.org/10.1115/1.4036640
[5] P. Kahhal, H. Ghorbani-Menghari, H.J. Kim, H. Choi, P.R. Cha, J.H. Kim, Metaheuristic optimization of powder size distribution in powder forming process using multi-particle finite element method coupled with artificial neural network and genetic algorithm, J. Mater. Trans. 64 (2023) 2648-2655. https://doi.org/10.2320/matertrans.MT-MI2022006
[6] P. Kahhal, J. Jung, Y.C. Hur, Y.H. Moon, J.H. Kim, Analysis of powder compaction process using multi-particle finite element method, J. Mater. Trans. 63 (2022) 1576-1582. https://doi.org/10.2320/matertrans.MT-MB2022012
[7] R.B. Carmona-Paredes, R. Domínguez-Mora, M.L. Arganis-Juárez, M.L., E. Juan-Diego, R. Mendoza-Ramírez, E. Carrizosa-Elizondo, use of evolutionary computation and guide curves to optimize the operating policies of a reservoir system established to supply drinking water, J. Appl. Water. Sci. 13 (2023) 2. https://doi.org/10.1007/s13201-022-01807-z