Modeling analysis of argon gas flow rate’s effect on pre-mixed powder separation in laser metal deposition process and experimental validation
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Pre-mixed powder is frequently used powder supply to fabricate functional gradient material by laser metal deposition (LMD). Argon gas flow blows the powder mixture following feeding pipe to melt pool in the LMD process. The powder mixture easily separates since the ingredient particles have different accelerations which are caused by different densities and sizes under the dynamic interaction with argon gas flow. This study investigated the argon gas flow rate’s effect on pre-mixed powder separation using modeling methodology. A CFD-DEM model, which gave consideration to both particle-particle collision and particle-argon gas flow interaction, was employed to simulate the pre-mixed powder flow in the feeding pipe. Three argon gas flow rates 6, 7, and 8 m/s were selected, analyzed, and compared based on their effects on powder mixture separations. Pre-mixed Cu and 4047 Al powders with equal volume percentages (50 to 50%) were investigated during their transportation process under three argon gas flow rates. All particles’ dynamic flow behaviors and the powder distributions were simulated and observed. The volume percentage of each kind of powder was plotted by quantifying the distribution of different particles after exiting the nozzle. It can be found that the intersection point of both powder volume percentages appeared increasingly earlier along with the increasing argon gas flow rate. To prove the correctness of the simulation results, the CFD-DEM model in this study was then validated by an experiment done by authors before. The results from this study are valuable contributions to the research of functionally graded material fabrication with pre-mixed powder through LMD process.
KeywordsLaser metal deposition Pre-mixed powder Powder separation Argon flow rate CFD-DEM model
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The authors gratefully acknowledge the financial support provided for this study by the NASA EP-SCoR Grant number NNX13AM99A.
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