USING AUTOMATED IMAGE ANALYSIS FOR CHARACTERIZATION OF ADDITIVE MANUFACTURING POWDERS

The metallic powders used as feedstock for the additive manufacturing (AM) process require evaluation of several morphological and chemical composition characteristics to ensure predictable material behavior during the manufacturing process and performance in use. The size, shape, and internal porosity of the particles are several traits requiring evaluation. Often, numerical estimates representing these characteristics are made using the digital images generated with X-ray computed tomography (CT) scans or automated image analysis (AIA). Image analysis offers several advantages over CT scans. These include lower equipment and individual test costs, higher image resolution due to higher system magnifications, and faster analysis time. In addition to the morphological analyses, particle-to-particle chemical composition uniformity is essential to maintaining the proper microstructure of the part. A procedure using a scanning electron microscope (SEM) equipped with an energy dispersive spectrometer (EDS) is described to determine the presence of particulate cross-product contamination and any foreign materials.