High-Throughput Unsupervised Profiling of the Morphology of 316L Powder Particles for Use in Additive Manufacturing

TL;DR

This paper introduces an automated machine learning framework for high-throughput, unsupervised profiling of metallic powder particle morphology, enabling rapid assessment and tracking for additive manufacturing quality control.

Contribution

It develops and evaluates three clustering pipelines, identifying Fourier-descriptor + k-means as most effective for large-scale powder shape analysis.

Findings

Fourier-descriptor + k-means pipeline achieves best clustering metrics.

Framework processes approximately 126,000 images with sub-millisecond per particle runtime.

Shape groups serve as a basis for future studies on powder flowability and part quality.

Abstract

Selective Laser Melting (SLM) is a powder-bed additive manufacturing technique whose part quality depends critically on feedstock morphology. However, conventional powder characterization methods are low-throughput and qualitative, failing to capture the heterogeneity of industrial-scale batches. We present an automated, machine learning framework that couples high-throughput imaging with shape extraction and clustering to profile metallic powder morphology at scale. We develop and evaluate three clustering pipelines: an autoencoder pipeline, a shape-descriptor pipeline, and a functional-data pipeline. Across a dataset of approximately 126,000 powder images (0.5-102 micrometer diameter), internal validity metrics identify the Fourier-descriptor + k-means pipeline as the most effective, achieving the lowest Davies-Bouldin index and highest Calinski-Harabasz score while maintaining sub-millisecond runtime per particle on a standard desktop workstation. Although the present work focuses on establishing the morphological-clustering framework, the resulting shape groups form a basis for future studies examining their relationship to flowability, packing density, and SLM part quality. Overall, this unsupervised learning framework enables rapid, automated assessment of powder morphology and supports tracking of shape evolution across reuse cycles, offering a path toward real-time feedstock monitoring in SLM workflows.