Powder characterisation is integral to powder metallurgy processes, including various powder-bed additive manufacturing processes. Typical powder feedstock properties of interest include the powder size distribution, powder morphology, and elemental composition; all this information provides critical insights into the feedstock’s influence on the material properties of the fabricated parts. Laser diffraction is one of the most established standard techniques to gauge particle size distribution (PSD) for AM process monitoring and control, offering a statistically significant PSD of the feedstock after a relatively short test. Nevertheless, one of the challenging tasks for conducting laser diffraction tests is to determine the refractive index (RI) of the powder that is being analysed; (Mie) scattering detectors can under or over-estimate the particle size if the wrong RI is chosen. This is especially tricky when mixed feedstock with different materials (and hence different RI) or smaller particle sizes are examined. Therefore, complementary techniques that involve extinction detectors have also been utilised to determine PSD and morphology based on the “shadow” cast by the particles. In this work, we expand on the use of Single Particle Optical Sizing (SPOS; combination of extinction and scattering detectors) to progress towards a wet powder characterisation technique based on an additional variable, time. By incorporating a suitable time-lapse analysis of the PSD, an additional layer of information can be obtained. The SPOS of the AM powders allows for better quantitative results (particles/mg) of both the coarse particles as well as the fine particles in the PSD, yielding much tighter process/parameter control for AM.This is especially useful for mixed feedstocks whereby the densities of the individual powder materials are relatively different. Importantly, this provides a supplementary pathway for information on the PSD of mixed feedstock, as we progress towards an era of greater interest in multi-material 3D and even 4D printing.
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Funding Info:
This research / project is supported by the A*STAR - RIE2020, INDUSTRY ALIGNMENT FUND - INDUSTRY COLLABORATION PROJECTS (IAF-ICP)
Grant Reference no. : I2001E0056