In a selective laser melting process, it is common to reuse the powder in consecutive cycles of the route because it is more sustainable and cost effective. However, it is unknown whether reusing the material has an influence on the process. In this paper, Inconel 718, Ti6Al4V, AlSi10Mg and Scalmalloy are characterized to determine the impact of reusing powders on the additive manufacturing (AM) process under an argon high-purity atmosphere. Virgin powders were taken from the suppliers and compared to powders that had been used in the process for a long period of time with periodic ‘rejuvenation’. A well-structured characterization procedure, combining many existing techniques, is proposed, determining changes in the morphology, composition (chemical and microstructure) and flowability. Clear differences between the virgin and used state are revealed by the characterizations; AlSi10Mg, appears to be the most sensitive to reuse with changes in particle size distribution and morphology, and with an increase in the oxygen content. The main contribution of this paper is providing insight into the effects of reuse for four commonly used AM powders, by means of a simple but well-structured method that links the particle feature characterization process to the flowability of metal AM powders. The provided insights enable enhanced decision-making on recycling and reuse of powder for specific AM processes.

One problematic task in the laser-based powder bed fusion (LB-PBF) process is the estimation of meltpool depth, which is a function of the process parameters and thermophysical properties of the materials. In this research, the effective factors that drive the meltpool depth such as optical penetration depth, angle of incidence, the ratio of laser power to scan speed, surface properties and plasma formation are discussed. The model is useful to estimate the meltpool depth for various manufacturing conditions. A proposed methodology is based on the simulation of a set of process parameters to obtain the variation of meltpool depth and temperature, followed by validation with reference to experimental test data. Numerical simulation of the LB-PBF process was performed using the computational scientific tool “Flow3D Version 11.2” to obtain the meltpool features. The simulation data was then developed into a predictive analytical model for meltpool depth and temperature based on the thermophysical powder properties and associated parameters. The novelty and contribution of this research are characterising the fundamental governing factors on meltpool depth and developing an analytical model based on process parameters and powder properties. The predictor model helps to accurately estimate the meltpool depth which is important and has to be sufficient to effectively fuse the powder to the build plate or the previously solidified layers ensuring proper bonding quality. Results showed that the developed analytical model has a high accuracy to predict the meltpool depth. The model is useful to rapidly estimate the optimal process window before setting up the manufacturing tasks and can therefore save on lead-time and cost. This methodology is generally applied to Inconel 718 processing and is generalisable for any powder of interest. The discussions identified how the effective physical factors govern the induced heat versus meltpool depth which can affect the bonding and the quality of LB-PBF components.