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<p>This study presents a dynamic modeling approach using Discrete Element Method (DEM) to optimize particle packing density in sinter-based additive manufacturing (AM), with a focus on Binder Jet Printing (BJP). High packing density is crucial to achieving high green density, minimizing shrinkage, and ensuring dimensional stability during sintering. Traditional models have provided approximations of packing density but lacked the capability to predict custom powder blends accurately. Here, DEM modeling uses particle size distributions (PSDs) from Scanning Electron Microscopy (SEM) and laser diffraction analyses to simulate packing density, with experimental validation on CP-Ti and Ti-6Al-4&#xa0;V blends. The DEM model, refined with friction adjustments to simulate the conditions of BJP and tap density, effectively predicts green and tap densities with less than 5% error. SEM-based PSDs demonstrate increased model performance and reliability in informing custom blend designs. Experimental blended powders achieved green densities above 70%, and sintered custom blends demonstrated that high density can be achieved at lower sintering temperatures, thereby reducing oxygen content and refining the microstructure. This method enables accurate, low-cost blend design, allowing optimized powder formulations without extensive physical testing. The findings confirm DEM modeling’s effectiveness for designing high-density powder blends (&gt; 70%) in sinter-based AM, contributing to improved quality and cost efficiency in manufacturing.</p>

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DEM modeling of particle size distribution and packing density and its application in Sinter-Based additive manufacturing

  • 期刊论文

  • 发表日期：

    2025年12月16日

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  • Nathan Jump,
  • Pei Sun,
  • Zhigang Zak Fang

摘要

This study presents a dynamic modeling approach using Discrete Element Method (DEM) to optimize particle packing density in sinter-based additive manufacturing (AM), with a focus on Binder Jet Printing (BJP). High packing density is crucial to achieving high green density, minimizing shrinkage, and ensuring dimensional stability during sintering. Traditional models have provided approximations of packing density but lacked the capability to predict custom powder blends accurately. Here, DEM modeling uses particle size distributions (PSDs) from Scanning Electron Microscopy (SEM) and laser diffraction analyses to simulate packing density, with experimental validation on CP-Ti and Ti-6Al-4 V blends. The DEM model, refined with friction adjustments to simulate the conditions of BJP and tap density, effectively predicts green and tap densities with less than 5% error. SEM-based PSDs demonstrate increased model performance and reliability in informing custom blend designs. Experimental blended powders achieved green densities above 70%, and sintered custom blends demonstrated that high density can be achieved at lower sintering temperatures, thereby reducing oxygen content and refining the microstructure. This method enables accurate, low-cost blend design, allowing optimized powder formulations without extensive physical testing. The findings confirm DEM modeling’s effectiveness for designing high-density powder blends (> 70%) in sinter-based AM, contributing to improved quality and cost efficiency in manufacturing.