In order to predict the process window of laser powder bed fusion (LPBF) for printing metallic components, the calculation of volumetric energy density (VED) has been widely calculated for controlling process parameters. However, because it is assumed that the process parameters contribute equally to heat input, the VED still has limitation for predicting the process window of LPBF-processed materials. In this study, an explainable machine learning (xML) approach was adopted to predict and understand the contribution of each process parameter to defect evolution in Ti alloys in the LPBF process. Various ML models were trained, and the Shapley additive explanation method was adopted to quantify the importance of each process parameter. This study can offer effective guidelines for fine-tuning process parameters to fabricate high-quality products using LPBF.

The emergence of ferrous-medium entropy alloys (FeMEAs) with excellent tensile properties represents a potential direction for designing alloys based on metastable engineering. In this study, an FeMEA is successfully fabricated using laser powder bed fusion (LPBF), a metal additive manufacturing technology. Tensile tests are conducted on the LPBF-processed FeMEA at room temperature and cryogenic temperatures (77 K). At 77 K, the LPBF-processed FeMEA exhibits high yield strength and excellent ultimate tensile strength through active deformation-induced martensitic transformation. Furthermore, due to the low stability of the face-centered cubic (FCC) phase of the LPBFprocessed FeMEA based on nano-scale solute heterogeneity, stress-induced martensitic transformation occurs, accompanied by the appearance of a yield point phenomenon during cryogenic tensile deformation. This study elucidates the origin of the yield point phenomenon and deformation behavior of the FeMEA at 77 K.

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.

The Ti-6Al-4V lattice structure is widely used in the aerospace industry owing to its high specific strength, specific stiffness, and energy absorption. The quality, performance, and surface roughness of the additively manufactured parts are significantly dependent on various process parameters. Therefore, it is important to study process parameter optimization for relative density and surface roughness control. Here, the part density and surface roughness are examined according to the hatching space, laser power, and scan rotation during laser-powder bed fusion (LPBF), and the optimal process parameters for LPBF are investigated. It has high density and low surface roughness in the specific process parameter ranges of hatching space (0.06–0.12 mm), laser power (225–325 W), and scan rotation (15°). In addition, to investigate the compressive behavior of the lattice structure, a finite element analysis is performed based on the homogenization method. Finite element analysis using the homogenization method indicates that the number of elements decreases from 437,710 to 27 and the analysis time decreases from 3,360 to 9 s. In addition, to verify the reliability of this method, stress–strain data from the compression test and analysis are compared.

Although the Ti–6Al–4V alloy has been used in the aircraft industry owing to its excellent mechanical properties and low density, the low formability of the alloy hinders broadening its applications. Recently, laser-powder bed fusion (L-PBF) has become a novel process for overcoming the limitations of the alloy (i.e., low formability), owing to the high degree of design freedom for the geometry of products having outstanding performance used in hightech applications. In this study, to investigate the effect of bulk shape on the microstructure and mechanical properties of L-PBFed Ti-6Al-4V alloys, two types of samples are fabricated using L-PBF: thick and thin samples. The thick sample exhibits lower strength and higher ductility than the thin sample owing to the larger grain size and lower residual dislocation density of the thick sample because of the heat input during the L-PBF process.

Powder flowability is critical in additive manufacturing processes, especially for laser powder bed fusion. Many powder features, such as powder size distribution, particle shape, surface roughness, and chemical composition, simultaneously affect the flow properties of a powder; however, the individual effect of each factor on powder flowability has not been comprehensively evaluated. In this study, the impact of particle shape (sphericity) on the rheological properties of Ti-6Al-4V powder is quantified using an FT4 powder rheometer. Dynamic image analysis is conducted on plasma-atomized (PA) and gas-atomized (GA) powders to evaluate their particle sphericity. PA and GA powders exhibit negligible differences in compressibility and permeability tests, but GA powder shows more cohesive behavior, especially in a dynamic state, because lower particle sphericity facilitates interaction between particles during the powder flow. These results provide guidelines for the manufacturing of advanced metal powders with excellent powder flowability for laser powder bed fusion.

In the powder bed fusion (PBF) process, a 3D shape is formed by the continuous stacking of very fine powder layers using computer-aided design (CAD) modeling data, following which laser irradiation can be used to fuse the layers forming the desired product. In this method, the main process parameters for manufacturing the desired 3D products are laser power, laser speed, powder form, powder size, laminated thickness, and laser diameter. Stainless steel (STS) 316L exhibits excellent strength at high temperatures, and is also corrosion resistant. Due to this, it is widely used in various additive manufacturing processes, and in the production of corrosion-resistant components with complicated shapes. In this study, rectangular specimens have been manufactured using STS 316L powder via the PBF process. Further, the effect of heat treatment at 800 °C on the microstructure and hardness has been investigated.

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.

In this study, the high-temperature oxidation properties of austenitic 316L stainless steel manufactured by laser powder bed fusion (LPBF) is investigated and compared with conventional 316L manufactured by hot rolling (HR). The initial microstructure of LPBF-SS316L exhibits a molten pool ~100 μm in size and grains grown along the building direction. Isotropic grains (~35 μm) are detected in the HR-SS316L. In high-temperature oxidation tests performed at 700oC and 900oC, LPBF-SS316L demonstrates slightly superior high-temperature oxidation resistance compared to HR-SS316L. After the initial oxidation at 700oC, shown as an increase in weight, almost no further oxidation is observed for both materials. At 900oC, the oxidation weight displays a parabolic trend and both materials exhibit similar behavior. However, at 1100oC, LPBF-SS316L oxidizes in a parabolic manner, but HR-SS316L shows a breakaway oxidation behavior. The oxide layers of LPBF-SS316L and HR-SS316L are mainly composed of Cr2O3, Febased oxides, and spinel phases. In LPBF-SS316L, a uniform Cr depletion region is observed, whereas a Cr depletion region appears at the grain boundary in HR-SS316L. It is evident from the results that the microstructure and the hightemperature oxidation characteristics and behavior are related.

A well-established characterization method is required in powder bed fusion (PBF) metal additive manufacturing, where metal powder is used. The characterization methods from the traditional powder metallurgy process are still being used. However, it is necessary to develop advanced methods of property evaluation with the advances in additive manufacturing technology. In this article, the characterization methods of powders for metal PBF are reviewed, and the recent research trends are introduced. Standardization status and specifications for metal powder for the PBF process which published by the ISO, ASTM, and MPIF are also covered. The establishment of powder characterization methods are expected to contribute to the metal powder industry and the advancement of additive manufacturing technology through the creation of related databases.

Powder characteristics, such as density, size, shape, thermal properties, and surface area, are of significant importance in the powder bed fusion (PBF) process. The powder required is exclusive for an efficient PBF process. In this study, the particle size distribution suitable for the powder bed fusion process was derived by modeling the PBF product using simulation software (GeoDict). The modeling was carried out by layering sintered powder with a large particle size distribution, with 50 μm being the largest particle size. The results of the simulation showed that the porosity decreased when the mean particle size of the powder was reduced or the standard deviation increased. The particle size distribution of prepared titanium powder by the atomization process was also studied. This study is expected to offer direction for studies related to powder production for additive manufacturing.