Ni-based superalloys are widely used for critical components in aerospace, defense, industrial power generation systems, and other applications. Clean superalloy powders and manufacturing processes, such as compaction and hot isostatic pressing, are essential for producing superalloy discs used in turbine engines, which operate under cyclic rotating loads and high-temperature conditions. In this study, the plasma rotating electrode process (PREP), one of the most promising methods for producing clean metallic powders, is employed to fabricate Ni-based superalloy powders. PREP leads to a larger powder size and narrower distribution compared to powders produced by vacuum induction melt gas atomization. An important finding is that highly spheroidized powders almost free of satellites, fractured, and deformed particles can be obtained by PREP, with significantly low oxygen content (approximately 50 ppm). Additionally, large grain size and surface inclusions should be further controlled during the PREP process to produce high-quality powder metallurgy parts.
In this study, we analyzed the structural and mechanical properties of aluminum foams fabricated using aluminum powders of varying sizes and mixtures. The effects of sintering and pore structure at each size on the integrity and mechanical properties of the foams were investigated. Structural characteristics were examined using scanning electron microscopy and micro–computed tomography, while mechanical properties were evaluated through compression testing. The experimental results demonstrated that smaller powder sizes improved foam integrity, reduced porosity and pore size, and resulted in thinner cell walls. In combination, these effects increased compressive strength as the powder size decreased. The findings of this study contribute to the understanding and improvement of the mechanical properties of aluminum foams and highlight their potential for use in a wide range of applications.
The present study introduces a machine learning approach for designing new aluminum alloys tailored for directed energy deposition additive manufacturing, achieving an optimal balance between hardness and conductivity. Utilizing a comprehensive database of powder compositions, process parameters, and material properties, predictive models—including an artificial neural network and a gradient boosting regression model, were developed. Additionally, a variational autoencoder was employed to model input data distributions and generate novel process data for aluminum-based powders. The similarity between the generated data and the experimental data was evaluated using K-nearest neighbor classification and t-distributed stochastic neighbor embedding, with accuracy and the F1-score as metrics. The results demonstrated a close alignment, with nearly 90% accuracy, in numerical metrics and data distribution patterns. This work highlights the potential of machine learning to extend beyond multi-property prediction, enabling the generation of innovative process data for material design.
High-entropy alloys (HEAs) exhibit complex phase formation behavior, challenging conventional predictive methods. This study presents a machine learning (ML) framework for phase prediction in HEAs, using a curated dataset of 648 experimentally characterized compositions and features derived from thermodynamic and electronic descriptors. Three classifiers—random forest, gradient boosting, and CatBoost—were trained and validated through cross-validation and testing. Gradient boosting achieved the highest accuracy, and valence electron concentration (VEC), atomic size mismatch (δ), and enthalpy of mixing (ΔHmix) were identified as the most influential features. The model predictions were experimentally verified using a non-equiatomic Al₃₀Cu₁₇.₅Fe₁₇.₅Cr₁₇.₅Mn₁₇.₅ alloy and the equiatomic Cantor alloy (CoCrFeMnNi), both of which showed strong agreement with predicted phase structures. The results demonstrate that combining physically informed feature engineering with ML enables accurate and generalizable phase prediction, supporting accelerated HEA design.
Friction Stir Spot Welding (FSSW) is a solid-state welding technology that is rapidly growing in the automotive industry. Achieving superior welding characteristics requires the proper selection of tool geometry and process conditions. In this study, FSSW was performed on dissimilar materials comprising AA5052-HO/hot-melt aluminum alloy sheets and Steel Plate Cold Rolled for Deep Drawing Use(SPCUD) steel sheets. The effects of tool geometry, plate arrangement, and tool plunge depth on the welding process were investigated. At the joint interface between the aluminum alloy and the steel sheet, new intermetallic compounds (IMCs) were observed. As the plunge depth increased, thicker and more continuous IMC layers were formed. However, excessive plunge depth led to discontinuous layers and cracking defects. An analysis of the IMCs revealed a correlation between the IMC thickness and the shear tensile load. Furthermore, compared to the conventional Al-Top arrangement, the St-Top arrangement exhibited reduced deformation and superior shear tensile load values. These findings indicate that plate arrangement significantly influences the mechanical properties of the joint.
Thermoelectric materials have been the focus of extensive research interest in recent years due to their potential in clean power generation from waste heat. Their conversion efficiency is primarily reflected by the dimensionless figure of merit, with higher values indicating better performance. There is a pressing need to discover materials that increase output power and improve performance, from the material level to device fabrication. This review provides a comprehensive analysis of recent advancements, such as Bi2Te3-based nanostructures that reduce thermal conductivity while maintaining electrical conductivity, GeTe-based high entropy alloys that utilize multiple elements for improved thermoelectric properties, porous metal-organic frameworks offering tunable structures, and organic/hybrid films that present low-cost, flexible solutions. Innovations in thermoelectric generator designs, such as asymmetrical geometries, segmented modules, and flexible devices, have further contributed to increased efficiency and output power. Together, these developments are paving the way for more effective thermoelectric technologies in sustainable energy generation.
This study focused on optimizing the digital light processing (DLP) 3D printing process for high-precision ceramic components using alumina-based slurries. Key challenges, such as cracking during debinding and precision loss due to slurry sedimentation, were addressed by evaluating the exposure time and the nano-to-micro alumina powder ratios. The optimal conditions—exposure time of 15 seconds and a 1:9 mixing ratio—minimized cracking, improved gas flow during debinding, and increased structural precision. Microchannels with diameters above 1.2 mm were successfully fabricated, but channels below 0.8 mm faced challenges due to slurry accumulation and over-curing. These results establish a reliable process for fabricating complex ceramic components with improved precision and structural stability. The findings have significant potential for applications in high-value industries, including aerospace, energy, and healthcare, by providing a foundation for the efficient and accurate production of advanced ceramic structures.
Light-weight ceramic insulation materials and high-emissivity coatings were fabricated for reusable thermal protection systems (TPS). Alumina-silica fibers and boric acid were used to fabricate the insulation, which was heat treated at 1250 °C. High-emissivity coating of borosilicate glass modified with TaSi2, MoSi2, and SiB6 was applied via dip-and-spray coating methods and heat-treated at 1100°C. Testing in a high-velocity oxygen fuel environment at temperatures over 1100 °C for 120 seconds showed that the rigid structures withstood the flame robustly. The coating effectively infiltrated into the fibers, confirmed by scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction analyses. Although some oxidation of TaSi2 occurred, thereby increasing the Ta2O5 and SiO2 phases, no significant phase changes or performance degradation were observed. These results demonstrate the potential of these materials for reusable TPS applications in extreme thermal environments.
Iron oxide (ε-Fe₂O₃) is emerging as a promising electromagnetic material due to its unique magnetic and electronic properties. This review focuses on the intrinsic properties of ε-Fe₂O₃, particularly its high coercivity, comparable to that of rare-earth magnets, which is attributed to its significant magnetic anisotropy. These properties render it highly suitable for applications in millimeter wave absorption and high-density magnetic storage media. Furthermore, its semiconducting behavior offers potential applications in photocatalytic hydrogen production. The review also explores various synthesis methods for fabricating ε-Fe₂O₃ as nanoparticles or thin films, emphasizing the optimization of purity and stability. By exploring and harnessing the properties of ε-Fe₂O₃, this study aims to contribute to the advancement of next-generation electromagnetic materials with potential applications in 6G wireless telecommunications, spintronics, high-density data storage, and energy technologies.
This study investigated whether calcium (Ca) addition improved the recovery of neodymium (Nd) and dysprosium (Dy) from Nd-Fe-B magnet scrap using magnesium (Mg)-based liquid metal extraction (LME). Traditional LME processes are limited to temperatures up to 850 °C due to oxidation issues, reducing the efficiency of rare earth element (REE) recovery, especially for Dy. By adding 10 wt.% Ca to Mg and increasing the processing temperature to 1,000 °C, we achieved nearly 100% Nd and approximately 38% Dy recovery, compared to 91% and 28%, respectively, with pure Mg at 850 °C. However, excessive Ca addition (20 wt.%) decreased the recovery efficiency due to the formation of stable intermetallic compounds. These results highlight the critical role of Ca in optimizing REE recycling from Nd-Fe-B magnet scrap.
This study investigated the optimal process conditions and mechanical properties of Cu-10Sn alloys produced by the powder bed fusion (PBF) method. The optimal PBF conditions were explored by producing samples with various laser scanning speeds and laser power. It was found that under optimized conditions, samples with a density close to the theoretical density could be fabricated using PBF without any serious defects. The microstructure and mechanical properties of samples produced under optimized conditions were investigated and compared with a commercial alloy produced by the conventional method. The hardness, maximum tensile strength, and elongation of the samples were significantly higher than those of the commercially available cast alloy with the same chemical composition. Based on these results, it is expected to be possible to use the PBF technique to manufacture Cu-10Sn products with complex 3D shapes that could not be made using the conventional manufacturing method.
Hot section components of gas turbines are exposed to a high operating temperature environment. To protect these components, thermal barrier coatings (TBC) are applied to their surfaces. Yttria-stabilized zirconia (YSZ), which is widely used as a TBC material, faces limitations at temperatures above 1200℃. To mitigate these issues, research has focused on adding lanthanide rare earth oxides and tetravalent oxides to prevent the phase-transformation of the monoclinic phase in zirconia. This study investigated the effects of varying TiO2 content in Nd2O3 and Yb2O3 co-doped YSZ composites. Increasing TiO2 content effectively suppressed formation of the monoclinic phase and increased the thermal degradation resistance compared to YSZ in environments over 1200℃. These findings will aid in developing more thermally stable and efficient TBC materials for application in high-temperature environments.
Aluminum-based composites are in high demand in industrial fields due to their light weight, high electrical conductivity, and corrosion resistance. Due to its unique advantages for composite fabrication, powder metallurgy is a crucial player in meeting this demand. However, the size and weight fraction of the reinforcement significantly influence the components' quality and performance. Understanding the correlation of these variables is crucial for building high-quality components. This study, therefore, investigated the correlations among various parameters—namely, milling time, reinforcement ratio, and size—that affect the composite’s physical and mechanical properties. An artificial neural network model was developed and showed the ability to correlate the processing parameters with the density, hardness, and tensile strength of Al2024-B4C composites. The predicted index of relative importance suggests that the milling time has the most substantial effect on fabricated components. This practical insight can be directly applied in the fabrication of high-quality Al2024-B4C composites.
We investigated the microstructure of an FeCrMnNiCo alloy fabricated by spark plasma sintering under different sintering temperatures (1000–1100°C) and times (1–600 s). All sintered alloys consisted of a single face-centered cubic phase. As the sintering time or temperature increased, the grains of the sintered alloys became partially coarse. The formation of Cr7C3 carbide occurred on the surface of the sintered alloys due to carbon diffusion from the graphite crucible. The depth of the layer containing Cr7C3 carbides increased to ~110 μm under severe sintering conditions (1100°C, 60 s). A molten zone was observed on the surface of the alloys sintered at higher temperatures (>1060°C) due to severe carbon diffusion that reduced the melting point of the alloy. The porosity of the sintered alloys decreased with increasing time at 1000°C, but increased at higher temperatures above 1060°C due to melting-induced porosity formation.
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Fabrication and Alloying Behavior of Ultra-Lightweight AlTiCrVMg High-Entropy Alloy via Al-Mg Mutual Solubility and Sintering Control Eunhyo Song, Hansung Lee, Byungmin Ahn Journal of Powder Materials.2025; 32(3): 254. CrossRef
This study explored the process-structure-property (PSP) relationships in Ti-6Al-4V alloys fabricated through direct energy deposition (DED) additive manufacturing. A systematic investigation was conducted to clarify how process variables—specifically, manipulating the cooling rate and energy input by adjusting the laser power and scan speed during the DED process—influenced the phase fractions, pore structures, and the resultant mechanical properties of the samples under various processing conditions. Significant links were found between the controlled process parameters and the structural and mechanical characteristics of the produced alloys. The findings of this research provide foundational knowledge that will drive the development of more effective and precise control strategies in additive manufacturing, thereby improving the performance and reliability of produced materials. This, in turn, promises to make significant contributions to both the advancement of additive manufacturing technologies and their applications in critical sectors.