Session
2026 Session 4
Location
Orem, UT
Start Date
5-4-2026 10:50 AM
Description
Additive Manufacturing (AM) methods, such as Binder Jetting (BJ), hold strong potential for in-situ manufacturing during extraterrestrial operations. During the BJ printing process, however, powdered material is easily dispersed as airborne particulates. These particulates—often < 10 μm in size—can travel significant distances from the source, presenting a potential hazard to operator safety and the longevity of the machine.
This work develops predictive models from the results previously published that quantified airborne particulate and surface accumulation of dust in BJ processes [1]. The models developed illustrate the dominating effects of gravity on particulate dispersion and corresponding surface mass accumulation rates, providing insights for micro-gravity environments.
A gaussian dispersion model is fit to the surface accumulation results of glass and steel as a function of material density ����, average dispersed particle size ����, and location x, y, and z, relative to the source. The models have an R2 of 0.70 and 0.95 for the results measured with and without air filtration, respectively. A secondary model is created to correlate the surface accumulation rate at varying distances to airborne particulate concentration levels using real-time air particulate monitoring. The correlation model has an R2=0.97.
Quantifying Powder Dispersion in Binder Jetting Based Additive Manufacturing Processes: Implications for Powder Handling and Additive Manufacturing in Microgravity Environments
Orem, UT
Additive Manufacturing (AM) methods, such as Binder Jetting (BJ), hold strong potential for in-situ manufacturing during extraterrestrial operations. During the BJ printing process, however, powdered material is easily dispersed as airborne particulates. These particulates—often < 10 μm in size—can travel significant distances from the source, presenting a potential hazard to operator safety and the longevity of the machine.
This work develops predictive models from the results previously published that quantified airborne particulate and surface accumulation of dust in BJ processes [1]. The models developed illustrate the dominating effects of gravity on particulate dispersion and corresponding surface mass accumulation rates, providing insights for micro-gravity environments.
A gaussian dispersion model is fit to the surface accumulation results of glass and steel as a function of material density ����, average dispersed particle size ����, and location x, y, and z, relative to the source. The models have an R2 of 0.70 and 0.95 for the results measured with and without air filtration, respectively. A secondary model is created to correlate the surface accumulation rate at varying distances to airborne particulate concentration levels using real-time air particulate monitoring. The correlation model has an R2=0.97.