In this study, machine learning models are proposed to predict the Vickers hardness of AlSi10Mg alloys fabricated by laser powder bed fusion (LPBF). A total of 113 utilizable datasets were collected from the literature. The hyperparameters of the machine-learning models were adjusted to select an accurate predictive model. The random forest regression (RFR) model showed the best performance compared to support vector regression, artificial neural networks, and k-nearest neighbors. The variable importance and prediction mechanisms of the RFR were discussed by Shapley additive explanation (SHAP). Aging time had the greatest influence on the Vickers hardness, followed by solution time, solution temperature, layer thickness, scan speed, power, aging temperature, average particle size, and hatching distance. Detailed prediction mechanisms for RFR are analyzed using SHAP dependence plots.

AlSi10Mg alloys are being actively studied through additive manufacturing for application in the automobile and aerospace industries because of their excellent mechanical properties. To obtain a consistently high quality product through additive manufacturing, studying the flowability and spreadability of the metal powder is necessary. AlSi10Mg powder easily forms an oxide film on the powder surface and has hydrophilic properties, making it vulnerable to moisture. Therefore, in this study, AlSi10Mg powder was hydrophobically modified through silane surface treatment to improve the flowability and spreadability by reducing the effects of moisture. The improved flowability according to the number of silane surface treatments was confirmed using a Carney flowmeter. In addition, to confirm the effects of improved spreadability, the powder prior to surface treatment and that subjected to surface treatment four times were measured and compared using s self-designed recoating tester. The results of this study confirmed the improved flowability and spreadability based on the modified metal powder from hydrophilic to hydrophobic for obtaining a highquality additive manufacturing product.

In this study, AlSi10Mg alloy powders are synthesized using gas atomization and sieving processes for powder bed fusion (PBF) additive manufacturing. The effect of nozzle diameter (ø = 4.0, 4.5, 5.0 and 8.0 mm) on the gas atomization and sieving size on the properties of the prepared powder are investigated. As the nozzle diameter decreases, the size of the manufactured powder decreases, and the uniformity of the particle size distribution improves. Therefore, the ø 4.0 mm nozzle diameter yields powder with superior properties. Spherically shaped powders can be prepared at a scale suitable for the PBF process with a particle size distribution of 10–45 μm. The Hausner ratio value of the powder is measured to be 1.24. In addition, the yield fraction of the powder prepared in this study is 26.6%, which is higher than the previously reported value of 10–15%. These results indicate that the nozzle diameter and the post-sieve process simultaneously influence the shape of the prepared powder as well as the satellite powder on its surface.

Aluminum (Al) - based powders have attracted attention as key materials for 3D printing because of their excellent specific mechanical strength, formability, and durability. Although many studies on the fabrication of 3Dprinted Al-based alloys have been reported, the influence of the size of raw powder materials on the bulk samples processed by selective laser melting (SLM) has not been fully investigated. In this study, AlSi10Mg powders of 65 μm in average particle size, prepared by a gas atomizing process, are additively manufactured by using an SLM process. AlSi10Mg powders of 45 μm average size are also fabricated into bulk samples in order to compare their properties. The processing parameters of laser power and scan speed are optimized to achieve densified AlSi10Mg alloys. The Vickers hardness value of the bulk sample prepared from 45 μm-sized powders is somewhat higher than that of the 65 μm-sized powder. Such differences in hardness are analyzed because the reduction in melt pool size stems from the rapid melting and solidification of small powders, compared to those of coarse powders, during the SLM process. These results show that the size of the powder should be considered in order to achieve optimization of the SLM process.