Adaptive Powder Behavior Model for Dispersion and Recovery in Metal Additive Manufacturing

Metal additive manufacturing relies on fine powders whose properties influence flow, spreading, and airborne release during processing, yet published data on powder characteristics, reuse effects, and emissions remain fragmented and difficult to compare. This study reviews quantitative measurements reported for metallic feedstocks used in laser powder bed fusion and directed energy deposition. A numerical evaluation model is developed to connect powder properties, process conditions, dispersion tendency, and material recovery. Particle size distribution values, density metrics, flow test results, reuse-related oxidation, and nanoparticle counts were compiled from the literature and normalized on a 0–1 scale. Four independent indices were defined: Material Fingerprint, process–powder interaction, airborne dispersion potential, and recovery. Adaptiveness refers to index sensitivity to changes in powder, reuse, and process conditions. The results indicate stable spreading for gas-atomized feedstocks, while wider particle size distributions and rougher surfaces increase cohesion and agglomeration, particularly under humid conditions and during reuse. Emission data indicate nanoparticle formation during processing, with recovery efficiency dependent on cyclone or high-efficiency particulate air filtration selection. The proposed model offers a screening approach for comparing powders and planning recovery strategies using data already available in the literature.