On the use of 2D moment invariants in the classification of additive manufacturing powder feedstock

Abstract 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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