Laser Powder Bed Fusion (L-PBF) is one of the most important metal additive manufacturing (AM) methods, with various applications in industries such as the medical and automotive sectors, where precision and customization are essential. This research emphasizes integrating machine learning (ML) techniques with experimental analyses to optimize L-PBF processes. It provides critical insights into the interplay among process parameters, microstructure, and mechanical performance. This study employs ML to model the relationship between process parameters and relative density in AISI 316L stainless steel components containing 2.5% copper, produced via L-PBF. Support Vector Regression (SVR) was identified as the most precise algorithm for predicting relative density, with an accuracy of over 99%, enabling the optimization of process parameters to achieve desired outcomes such as high density, improved surface quality, or enhanced productivity. Subsequently, microstructural and mechanical properties were analyzed to provide deeper insights into material behavior. Microstructural investigations using Scanning Electron Microscopy (SEM) and Optical Microscopy (OM) revealed substantial transformations, including forming equiaxed and columnar cells attributed to copper addition. Irregular grains were observed, resulting from the rapid solidification characteristic of the L-PBF process. Notably, copper fully dissolved into the austenitic phase with no evidence of segregation, leading to increased lattice distortion, reduced crystallite size, and enhanced hardness. Melt pool dimensions were analyzed across samples with varying process parameters, establishing correlations with porosity levels and microstructural refinement. Additionally, in-situ alloying with copper was found to improve mechanical properties slightly. Tensile testing further explored the relationship between porosity and mechanical properties, providing a comprehensive understanding of the impact of process parameters and material composition on overall performance. SEM analysis of the fracture surfaces identified both brittle and ductile failure mechanisms. Brittle fractures exhibited quasi-cleavage planes, likely aligning with melt pool boundaries, while ductile fractures displayed extensive dimple networks.

Metal additive manufacturing (AM) revolutionized the fabrication of complex metal components, providing remarkable precision and flexibility in producing complex geometries. Integrating artificial intelligence (AI) can further revolutionize this field by highlighting complex relationships within manufacturing systems and enhancing quality control. Machine learning (ML) methods provide innovative solutions to optimize resource consumption, improve process efficiency, and address manufacturing challenges by correlating process parameters, material properties, part geometry, microstructural characteristics, and their resultant properties. In metal AM processes, ML applications extend beyond process optimization to include defect detection, in situ monitoring, and the enhancement of manufacturability and repeatability of components. This study optimizes key process parameters in laser powder bed fusion (L-PBF) to correlate the processing parameters and defect content in AISI 316L-2.5%Cu components. By applying ML algorithms, this research identifies optimal process parameter combinations to achieve specific objectives such as high production rate, low defect content, or superior surface quality. Seven ML algorithms (Bayesian Regression, Decision Tree Regression, Gradient Boosting Regression, Gaussian Process Regression, K-Nearest Neighbors Regression, Random Forest Regression, and Support Vector Regression) were systematically evaluated for their predictive accuracy across varying training and testing dataset sizes. Support Vector Regression (SVR) with a training size of 80% was chosen as the most accurate model for relative density prediction, with an average error of 0.62%. The optimized process parameters, derived from the best-performing ML model prediction, demonstrated a precise relationship between process parameters and defect content for achieving relative density values above 99.5% or high productivity. The optimized parameters obtained from this approach highlight the potential of ML-driven methodologies to balance productivity and defect content in AM processes. These findings demonstrate the importance of ML in advancing L-PBF technology and its broader applicability in metal AM.

Due to its high hydrogen capacity (10.1 wt.%), aluminium hydride (AlH₃) is considered as a possible material for on-board hydrogen storage applications. However, several factors, such as a high decomposition temperature and sluggish desorption kinetics, limit this benefit and render this material unmarketable. To overcome these limitations, numerous studies have been conducted, such as using mechanical ball milling to reduce the particle size and adding dopants or catalysts. In this work, the effect of metal (Ti) on the dehydrogenation properties of AlH₃ has been investigated for the first time. The results show that Ti lowered the initial decomposition temperature and sped up the process of AlH₃ desorption. The 10 wt.% Ti-doped AlH₃ sample's initial decomposition temperature dropped from 145 °C to 120 °C compared to that of as-received AlH₃. For the desorption kinetic measurements at 100 °C, the 10 wt.% Ti-doped AlH₃ sample could desorb about 4.0 wt.% of H₂ in 20 min compared to 0.1 wt.% for the as-received AlH₃. After Ti was added, the activation energy for the dehydrogenation process of AlH₃ that was determined by Kissinger analysis decreased. From the X-ray diffraction analysis, we found that Ti did not react with AlH₃ during the mechanical milling and heating (desorption) processes. Ti is believed to play a catalytic role by inducing Ti-H interaction and weakening Al-H bonding, thus improving the dehydrogenation properties of AlH₃.

In the field of permanent magnets, there is growing interest in additive manufacturing techniques and, in terms of materials, in recycling rare earth elements. In collaborative projects (Magneco CPP2023-010653, Llavor-0005) between the university and industry, permanent magnets have been synthesized using stereolithography or pellet extrusion. Additive manufacturing techniques favor the efficient creation of complex components, reducing costs and production times, improving quality, and allowing greater customization in diverse industries such as aerospace, the automotive industry, and renewable energy. In the industrial field, the creation/generation of optimized and sustainable designs for permanent magnets is expected, reducing dependence on rare earth elements and recycling obsolete materials, as well as new sustainable and scalable manufacturing processes. It is a project in development in which the optimization of processes and materials shows the need for interaction between academia and companies, requiring the complete characterization (morphological, compositional, chemical, mechanical, thermal, magnetic) of both materials and components. One of the problems to be avoided is the oxidation of recycled powder particles, which makes their reconditioning necessary. Regarding the components, the achievement of sufficient densification and magnetic response (saturation magnetization, coercivity) is necessary. The influence of the controlled application of an external magnetic field (assisted magnetic field and additive manufacturing) is also analyzed during the manufacturing process.

High-entropy alloys (HEAs) are emerging materials that have recently been considered for biomedical applications. However, further adjustments in their properties are still needed to potentialize this application. Thus, research on novel processing routes is interesting for producing such advanced biomedical HEAs. In this study, we synthesized a novel metal–matrix composite (MMC) using an HEA as the matrix, which was reinforced with TiB and TiC particles. The sample was produced by argon arc melting a TiNbZrTaMo ingot with B₄C powder to induce in situ reactions. The resulting sample underwent extensive evaluations, including physical, chemical, structural, microstructural, mechanical, tribological, and biological assessments, and was compared to the unreinforced HEA sample. X-ray diffraction confirmed the in situ reactions, revealing a dual-phase (BCC + HCP) matrix with TiC and TiB peaks. Scanning and transmission electron microscopy showed that the BCC phase was enriched with refractory metals (Ta, Mo, and Nb), while the HCP and carbide precipitate phases were enriched with Ti and Zr. The results demonstrated strong interfacial bonding between the TiC precipitate and the matrix. Mechanical property analysis indicated that the precipitates retained a combination of high microhardness and a relatively low elastic modulus. Despite this, the precipitates enhanced wear resistance without offering any benefits in terms of corrosion resistance. Cytotoxicity tests showed that the precipitates positively influenced cell viability and adhesion. These findings highlight the potential for developing novel biomedical materials by combining HEA and MMC concepts. (Financial support: CNPq and FAPESP grant #2024/03148-3)

The electronic structure of the Sb-based intermetallic compound Gd₄Sb₃ has been studied, and it was recently revealed to have half-metallic properties [1]. Experimentally, anomalous Nernst and Hall effects were also found in Gd₄Sb_3, which confirmed the half-metallic behavior and anomalous electronic structure of this intermetallic compound [2]. Our calculations were performed according to an ab initio GGA+U method, which accounted for the strong electron correlations in the 4f shell of Gd. The ferromagnetic ordering in Gd₄Sb₃ is characterized by the large magnetic moment equal to 31 μ_B / f. u., which is mostly formed by the magnetic moments of the Gd ions. The band structure of Gd₄Sb₃ has interesting features which were revealed by our spin-polarized calculations. We found that one spin projection had completely metallic properties with multiple bands crossing the Fermi energy, whereas the other one was found to have an energy gap of 0.67 eV. Such features are absent in the binary semimetallic GdSb or metallic GdSb₂ compound with the close composition, which makes Gd₄Sb₃ a new, interesting half-metal ferromagnet with full spin polarization [1]. This research was supported by the Russian Science Foundation pr. No. 22-42-02021.

[1] Baidak, S. T.; Lukoyanov, A. V. Semimetallic, half-metallic, semiconducting, and metallic states in Gd-Sb compounds. Int. J. Mol. Sci. 2023, 24(10), 8778.

[2] Han, Y.; Qiu, C.; Ren, W.; et al. Anomalous Hall and Nernst effects in the half-metallic ferromagnet Gd₄Sb₃. Phys. Rev. B 2024, 110, 144405.

As of late, there has been increasing scope and demand for implants in the biomedical sector, notably in the field of orthopaedics. Within this field, zinc (Zn) is a promising base metal for such implants due to its combination of biocompatibility, mechanical properties, and corrosion response.

For the first time, pure Zn was successfully synthesized using the Disintegrated Melt Deposition (DMD) method and subjected to systematic cryogenic treatment (CT) study, with exposure to varying subzero temperatures (− 20 °C, − 50 °C, − 80 °C, and − 196 °C) for a duration of 24 hours. Densification occurred for all materials (with a 35.9% porosity reduction after exposure to − 50 °C being the most significant), . Microstructurally, CT induced significant grain growth across all exposure temperatures, with − 80 °C conferring the largest grain size (224% increase over as-extruded equivalent). The compression response was also improved slightly after exposure to − 50 °C, with improvements of 2.7%, 2.3%, and 1.0% to compressive yield strength, ultimate compressive strength, and work of fracture, respectively. Exposure to − 196 °C also notably lowered corrosion rates (32.4% reduction compared to as-extruded equivalent).

These findings highlight the ability of CT to not just alter but tailor the individual properties ofZn-based materials, useful in specific applications. Furthermore, this also opens up a new research area for this Hexagonal Closed Pack (HCP) metal and its derivatives.

Introduction

Rising energy demands and environmental challenges necessitate advancements in nuclear reactor materials to endure harsh conditions, including high temperatures, neutron flux, and oxidation. Medium-entropy alloys (MEAs) offer promising properties like enhanced strength, ductility, and radiation resistance. This study focuses on designing FeCrV-based refractory MEAs for nuclear applications using multi-scale modelling and experimental validation, leveraging the potential of DFT and machine learning to optimize performance and simulate large-scale behaviours.

Methods

First-principle calculations were performed using VASP, employing the PAW method and PBE-GGA for accurate modelling. AIMD simulations with a 0.5 fs timestep generated training data under constant and increasing temperatures. Neural network potentials were trained using DeepPot-SE. Large-scale MD simulations applied periodic boundaries and NPT equilibration at 300 K-1023 K. MC/MD methods facilitated atom exchanges, governed by Boltzmann probability, to explore temperature-dependent behaviours and structural evolution.

Results

XRD and TEM microstructure analyses revealed that the RMEA comprises two distinct phases: BCC1, the nominal alloy phase, and BCC2, a vanadium-rich phase. These two phases were also identified in MD/MC simulations conducted using the DNNP, demonstrating the accuracy of the model. The addition of 8% W to FeCr₂V significantly enhances its mechanical properties, achieving an ultimate compression strength of 1700 MPa and a Young’s modulus of 255.28 GPa. According to the DFT results, these improvements are attributed to a balanced interaction between metallic and covalent bonding, producing a highly irradiation-resistant material with an average vacancy formation energy of 2.66 eV. Dislocation analysis conducted via MD revealed that increasing the temperature enhances dislocation mobility, which further improves the ductility of the alloy.

Conclusion

In conclusion, a cost-effective FeCr₂V-based RMEA with superior mechanical properties, including high strength, ductility, and irradiation resistance, was successfully designed. With properties surpassing HEAs, this alloy demonstrates improved dislocation mobility at elevated temperatures, highlighting its potential for advanced structural applications in extreme environments.

X70 steel, a high-strength low-alloy (HSLA) steel, is extensively utilized in energy transport pipelines due to its exceptional mechanical properties, including robustness, ductility, and resistance to high-pressure environments. Despite these advantages, X70 steel is susceptible to crevice corrosion, a localized form of corrosion that occurs in confined spaces, such as overlaps, joints, or gaps. This type of corrosion can severely compromise the structural integrity and longevity of pipelines, leading to costly maintenance and safety concerns.

This study focuses on understanding the complex mechanisms underlying the crevice corrosion of X70 steel when exposed to a 0.3 M NaCl solution. A two-dimensional multi-physics finite element model was employed to simulate the corrosion process, providing valuable insights into the electrochemical phenomena involved.

The findings indicate a heterogeneous distribution of electrochemical potential within the crevice, accompanied by an exponential decrease in current density as the depth increases. This behavior is largely attributed to the ohmic drop (IR) effect. Additionally, geometric deformations were observed to progressively intensify, with the most severe damage occurring near the crevice opening. These results emphasize the need for improved designs and mitigation strategies to minimize the risks associated with crevice corrosion in X70 steel pipelines, thereby enhancing their safety, longevity, and durability in service.

Selective laser melting (SLM) has emerged as a versatile manufacturing method for complex metal components, offering high material utilization. 17-4PH stainless steel, which is known for its high strength and excellent corrosion resistance, is widely used in aerospace, biomedical, and mechanical manufacturing fields. However, the SLM process often results in microstructural features such as porosity and solute segregation, which significantly impact the wear and corrosion resistance of the produced components. Existing studies have primarily focused on the combined "solution heat treatment + aging" process, with limited research on individual solution or aging treatments. Furthermore, comparative studies on the effects of different heat treatment methods on wear and corrosion resistance remain insufficient. This study investigates the effects of three heat treatment methods—solution heat treatment (ST, 1 hour at 1040°C), aging treatment (AG, 1 hour at 480°C), and combined treatment (ST+AG, 1 hour at 1040°C followed by 1 hour at 480°C)—on the hardness, wear resistance, and corrosion resistance of SLM-manufactured 17-4PH stainless steel. The results reveal that aging treatment alone significantly enhances hardness, wear resistance, and corrosion resistance by optimizing the microstructure. Solution heat treatment improves microstructural uniformity and corrosion resistance but reduces wear resistance due to the complete transformation to a predominantly BCC structure. Combined treatment increases hardness but decreases wear and corrosion resistance due to precipitate formation. This study highlights the potential of aging treatment alone to enhance the performance of SLM-manufactured 17-4PH stainless steel, providing valuable experimental evidence for optimizing heat treatment strategies.