In-flight droplet plasma atomization: A novel method for preparing ultrafine spherical powders

Ultrafine spherical powders (USP) with particle sizes below 45 mu m are essential for various advanced manufacturing processes, including 3D printing, metal injection molding, cold spray, etc. However, conventional methods for preparing spherical powders are characterized by a broad particle size distribution (PSD), which reduces the yield of ultrafine powders and increases production costs. To address this challenge, a novel method named in-flight droplet plasma atomization (IDPA) has been developed. This method involves three key stages, i.e., formulation of large droplet, generation of high-temperature atomization fluid, and breakup of large droplet. As the core of the IDPA process, the breakup of droplets significantly impacts the PSD, which is predominantly regulated by the flow-controlling nozzle. To elucidate the underlying mechanisms of the large droplet breakup, this study firstly investigates the operational sustainability through experimental analysis of electro-thermal characteristics and corresponding temperature distribution of the flow-controlling nozzle. Results show that maintaining the nozzle temperature within an optimal range is important to avoid clogging and reduce mechanical wear, thus ensuring continuous operation of the IDPA process. Then, a numerical simulation model of the large droplet breakup process within the flow-controlling nozzle was developed to investigate the USP formation and facilitate the prediction of PSD. By employing the IDPA method to prepare USP, ultrafine particle size with a d90 of 27.97 mu m, PSD within a range from 5.27 to 56.2 mu m, high spheroidization ratio approaching 100 %, and the complete absence of hollow powders have been achieved in the prepared atomized powders. It's proved that IDPA is a viable and efficient approach for the production of USP. (c) 2024 The Society of Powder Technology Japan. Published by Elsevier BV and The Society of Powder Technology Japan. All rights are reserved, including those for text and data mining, AI training, and similar technologies.