The influence of MgO addition on the densification and microstructure of alumina (Al₂O₃) was studied. Compacted alumina specimens were manufactured using ball-milling and one-directional pressing followed by sintering at temperatures below 1700oC. Relative density, shrinkage, hardness, and microstructure were investigated using analytical tools such as FE-SEM, EDS, and XRD. When the MgO was added up to 5.0 wt% and sintered at 1500°C and 1600°C, the relative density exhibited an average value of 97% or more at both temperatures. The maximum density of 99.2% was with the addition of 0.5 wt% MgO at 1500°C. Meanwhile, the specimens showed significantly lower density values when sintered at 1400°C than at 1500°C and 1600°C owing to the relatively low sintering temperature. The hardness and shrinkage data also showed a similar trend in the change in density, implying that the addition of approximately 0.5 wt% MgO can promote the densification of Al₂O₃. Studying the microstructure confirmed the uniformity of the sintered alumina. These results can be used as basic compositional data for the development of MgOcontaining alumina as high-dielectric insulators.

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.

Conventionally, metal materials are produced by subtractive manufacturing followed by melting. However, there has been an increasing interest in additive manufacturing, especially metal 3D printing technology, which is relatively inexpensive because of the absence of complicated processing steps. In this study, we focus on the effect of varying powder size on the synthesis quality, and suggest optimum process conditions for the preparation of AlCrFeNi high-entropy alloy powder. The SEM image of the as-fabricated specimens show countless, fine, as-synthesized powders. Furthermore, we have examined the phase and microstructure before and after 3D printing, and found that there are no noticeable changes in the phase or microstructure. However, it was determined that the larger the powder size, the better the Vickers hardness of the material. This study sheds light on the optimization of process conditions in the metal 3D printing field.

In this study, Fe-Cu-Ni-Mo-C low alloy steel powder is consolidated by spark plasma sintering (SPS) process. The internal structure and the surface fracture behavior are studied using field-emission scanning electron microscopy and optical microscopy techniques. The bulk samples are polished and etched in order to observe the internal structure. The sample sintered at 900°C with holding time of 10 minutes achieves nearly full density of 98.9% while the density of the as-received conventionally sintered product is 90.3%. The fracture microstructures indicate that the sample prepared at 900°C by the SPS process is hard to break out because of the presence of both grain boundaries and internal particle fractures. Moreover, the lamellar pearlite structure is also observed in this sample. The samples sintered at 1000 and 1100°C exhibit a large number of tiny particles and pores due to the melting of Cu and aggregation of the alloy elements during the SPS process. The highest hardness value of 296.52 HV is observed for the sample sintered at 900°C with holding time of 10 minutes.

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