Computer vision is a branch of computer science that develops computational methods to extract information from visual images. Familiar applications of computer vision include facial recognition on Facebook, Google image search, and autonomous vehicle navigation. Computer vision and its related field, machine vision, are also widely used in manufacturing, particularly in robotics, process control, and quality inspection.
Powder bed additive manufacturing (AM) is an emerging technology for three-dimensional (3D) metal printing. The process is conceptually simple. A layer of fine metal powder is spread on the build plate. By using a laser or electron beam, powder particles are melted and resolidified. Another layer of powder is spread, and the process repeats multiple times to build up the final part layer by layer.
Among the many challenges in deploying this new manufacturing system are numerous metal powder-related issues centered on understanding how the physical characteristics of the powder (size, shape, and surface character) affect processing parameters (flowability and spreadability) and build outcomes (porosity and flaws). Fundamental to understanding these relationships is effectively characterizing the powders themselves.
To date, characterization of AM powder feedstocks has relied on direct measurements of powder properties of interest. For example, Strondl et al. used dynamic image analysis to capture photomicrographs of powders, segment them, and measure particle size and aspect ratio distributions. These quantities, along with powder rheology measurements, were found to correlate with powder flow and spreading characteristics. A group of case studies by Clayton et al. concluded that particle size distribution alone is insufficient to determine powder properties. Instead, they characterized powders by using rheological measurements, which they found to correlate with powder properties such as the degree of recycling, the manufacturing method, or the presence of additives. In perhaps the most comprehensive study of its kind, Slotwinski et al. systematically characterized virgin and recycled stainless steel and cobalt chrome powders in an effort to develop standards for AM feedstock materials. They measured particle size and shape with laser diffraction, x-ray computed tomography, and optical and scanning electron microscopy. In addition, they determined atomic structure and composition via energy-dispersive element x-ray analysis, x-ray photoelectron spectroscopy, and x-ray diffraction. Finally, Nandwana et al. studied particle size, flowability, and chemistry for two powders used in electron beam AM. Recycling caused significant changes to chemistry in one powder but minimal changes in the other; particle size and flowability were unaffected by recycling. Measurements such as these provide valuable insight into the factors that influence powder properties; nevertheless, data science offers a complementary approach that can extract information from a data stream directly, without reductive measurement.
In this article, we explore applications of computer vision for autonomously evaluating powder raw materials for metal AM. Instead of explicitly identifying and measuring individual particles, our method implicitly characterizes powder micrographs as a distribution of local image features. We demonstrate that the computer vision system is capable of classifying powders with different distributions of particle size, shape, and surface texture, as well as of identifying both representative and atypical powder images. AM applications include powder batch qualification, quantifying the effects of powder recycling, selecting build parameters based on powder characteristics, identifying features that might be associated with powder spreading or build flaws, and defining objective material standards based on visual images.
Finally, we note that a significant advantage of the computer vision approach is that it is an autonomous and objective system that does not require a subjective human judgment about what to measure or how to measure it. It is not limited to powder micrographs and in fact can be extended to new image data sets, including bulk microstructural images, without customization.