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.

Available for download on Tuesday, May 04, 2027

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May 4th, 10:50 AM

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.