Intelligent classification for three-dimensional metal powder particles

• Published in: Powder technology 2022-01, Vol.397, p.117018, Article 117018
• Main Authors: Zhou, Xin, Dai, Ning, Cheng, Xiaosheng, Thompson, Adam, Leach, Richard
• Format: Article
• Language: English
• Subjects: Additive manufacturing; Classification; Computed tomography; Machine learning; Mechanical properties; Metal particles; Metal powders; Metallurgical constituents; Metals; Particle shape; Powder; Segmentation; Three-dimensional shape measurement
• ISSN: 0032-5910; 1873-328X
• DOI: 10.1016/j.powtec.2021.11.062

The shape of constituent metal particles has a significant influence on the bulk properties of raw powder and the mechanical properties of manufactured parts. In this paper, we provide a method for automatic shape-based classification of metal powder particles. Firstly, X-ray computed tomography was used to obtain three-dimensional volume data of metal powder particles, and a segmentation operation was performed to separate particles from one another. Classifying by particle shape, particles were manually labelled to create training and testing data, separating the powder particles into one of six user-defined categories: ‘connected’, ‘ellipsoidal’, ‘irregular’, ‘pear’, ‘porous’ and ‘spherical’. The machine learning network PointNet++ was used to classify particles, using 1024 automatically-defined geometric particle features, as well as twelve user-defined features. Our results show that the accuracy of this automatic classification method reaches 93.8%. Finally, an additional test batch of measured powder was used to verify our classification method. [Display omitted] •Introduce machine learning method into 3D metal powder particles classification.•The classification results are visualized and verified on a batch of powders.•The method provides an efficient tool for accurate and automated characterization.