Multiscale Assessment on the Quality of Metal Powder Feedstocks for Additive Manufacturing 

Presented November 11, 2021

Additive manufacturing (AM) is attractive for producing parts with access to unprecedented geometry/configuration complexity and material composition gradient control, which are not attainable by traditional processes. However, the long-term success of this rapidly developing technology hinges, to a large degree, on the ability to make metal AM components reliably. Reducing defects is of primary importance to attain AM metal parts with mechanical strength and fatigue life approaching forged parts. 

Defects in AM parts fall into two main categories: porosity and crack. These can be incurred by various mechanisms: lack of fusion, keyhole collapse, gas porosity, balling, solidification cracking, solid-state cracking, and surface-connected porosity. Defects can also result from entrapment of impurities. Defects arise from the interplay between feedstock and AM beam energy, which involves complex, transient thermophysical and chemical processes. As such, defects can be equipment-, process-, and feedstock-related. Consequently, defect reduction starts with the quality control of feedstocks. 

Two mainstream AM technologies, i.e. laser powder bed fusion and blown powder direct energy deposition, use spherical powder feedstocks with the desired compositions and particle sizes, typically in the range of 10 to 50 microns. In this kind of matter in particulate form, the population of surface and sub-surface atoms represent four to five orders larger ratio as compared to bulk materials. While bulk chemistry still lays the foundation for ultimate mechanical properties, the surface chemistry of micro sized powders is becoming equally important, if not greater, for the quality of finished AM parts. Driven by the increased surface free energy (i.e., thermodynamic favorable) and the decrease in diffusion length (i.e., kinetic favorable), the particle-to-particle uniformity of AM metal powder is inherently susceptible to local events such as surface contamination, agglomeration, and composition variation, during powder manufacturing, packaging, storage, use and reuse.  

As defects arise in multiple length scales, it calls for analytical solutions with multi-length scale sampling capability. Numerous institutes have engaged in standardizing testing methods for AM powder feedstocks. This webinar is not intended to repeat these efforts; rather, we focus on selecting a few techniques, each with certain length-scale sampling capability, for identifying and quantitatively assessing the quality of AM metal powder feedstocks, e.g.  Ti6Al4V AM powder. Combination of these techniques (Scheme 1) enable multiscale sampling and uniformity assessment of AM metal powder feedstock: 

  • from single particle to 107-8 particles 
  • from a few surface atomic layers (nm-depth) to bulk (10s-µm depth) 
  • from elemental information to valence chemistries, and 
  • from %-level composition elements to sub-ppm trace impurities

AM webinar 1

In this webinar, we will cover:

  • Surface technique (AES, TOF-SIMS, and XPS) for single particle surface chemistry, oxide film composition and thicknessto particle-to-particle  consistency assessment from physiosorbed species to chemistry of top 100 nm that are responsible for defects in AM parts.  
  • Bulk chemical analysis technique (ICP-OES/MS, IGA and GDMS) for accurate composition analysis and trace impurity analysis at 107-8 particle scale 
  • Fractional IGA analysis to assess O, N and H chemistry, and moisture and residual -OH content in sub-% and ppm, at 107-8 particle scale.  

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About the Presenters:

Xinwei Wang

Xinwei Wang, Ph.D., Project Manager

As a material scientist and electrochemist with over a decade experience in 3rd party materials testing, Dr. Xinwei Wang’s recent focus has been on technical consultation and project management related to materials purification and doping, contamination-free manufacturing and regulatory compliance. His specialty lies on employing various analytical techniques to survey impurity type, statistically evaluate impurity level and distribution, and identify the contamination source for manufacturing and use of a broad range of high purity materials going into energy storage devices, semiconductors, jet engines, medical devices, additive manufacturing and photovoltaic cells.