期刊
MATERIALS CHARACTERIZATION
卷 149, 期 -, 页码 255-263出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.matchar.2019.01.019
关键词
Additive manufacturing; Feedstock powder; Moment invariant; Shape analysis
类别
资金
- Office of Naval Research (ONR) [N00014-16-1-2821]
- Materials Characterization Facility at Carnegie Mellon University [MCF-677785]
- United States National Science Foundation [DMR-1507830]
The shapes of near-spherical individual powder particles used as additive manufacturing feedstock are analyzed by means of moment invariants in combination with dimensionality reduction techniques, including t-distributed stochastic neighbor embedding (t-SNE) and hierarchical density-based clustering with noise (HDBSCAN). Affine Cartesian invariants up to the 12th order are found to be the most effective shape descriptors, in particular when used with t-SNE mapping. The methodology described in this paper can capture outlier particle shapes, can distinguish between very similar particle shapes, and has the potential to be implemented as a fully automated classification process to assess to what extent the distribution of the invariants deviates from the expected distribution for a powder that is known to have good additive manufacturing properties.
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