Improving the wear resistance of lightweight aluminum alloys is important for extending the service reliability of components. Laser powder bed fusion (L-PBF) offers a promising route for fabricating wear-resistant Al alloys through microstructural architecture control. In this study, an Al–Ce–Si–Mg alloy was fabricated by L-PBF, exhibiting a continuous nanoscale Al11Ce3 network embedded in the α-Al matrix. The as-built Al–Ce–Si–Mg alloy exhibited a lower wear rate than commercial 6061, 7075, and as-cast Al–Ce–Si–Mg alloys under dry reciprocating sliding conditions. By comparing the as-built sample with a spheroidized counterpart containing isolated intermetallic particles, this study demonstrates that the continuous intermetallic network acting as a strengthening skeleton can enhance resistance to plastic deformation and regulate wear-induced microstructure evolution. The network structure changes the subsurface evolution pathway from dynamic recrystallization-dominated grain refinement to boundary-mediated nanolaminated substructure formation during sliding. Micropillar compression further confirmed that the nanolaminated structure in the as-built alloy exhibited much higher yield strength and strain-hardening capacity, thereby helping to stabilize the protective oxide nanocomposite layer and enhance wear resistance. These findings provide a microstructure design strategy for wear-resistant additively manufactured Al alloys.

Dimensional deformation and shrinkage porosity are key investment casting defects that typically occur during solidification of the casting at ambient environment after high-temperature pouring. Although these two indicators are caused by various reasons, it is believed that the initial temperature of the shell is one of the dominant factors. This study leverages experiment and simulation methods to reveal the relationship between the temperature drop of ceramic shells and the dimensional deformation and shrinkage porosity of castings in CoCrMo alloy investment casting joints. The experimental results indicate that the insulation conditions of ceramic shells and the filling of high-temperature melt have a significant impact on the temperature drop curve of ceramic shells. The temperature drops of ceramic shells insulated with asbestos are much slower than that of ceramic shells without asbestos insulation, which has a significant impact on the dimensional deformation and shrinkage porosity of castings. Numerical simulation was conducted using a model calibrated for ceramic shell temperature, and the results showed that with the shell preheating temperature increasing from 800 to 1000 °C, the total shrinkage volume in the casting decreased from 4.19 to 2.38 cm3, the total displacement of the casting decreased from 1.2 to 1.0 mm, and the gap width decreased from 0.774 to 0.651 mm.

Creep-induced failures threaten the reliability of Al-based hot-end components. Despite advances in ML-aided design of heat-resistant Al alloys that have alleviated experimental and modeling burdens, creep tolerance metrics are sporadically distributed across heterogeneous modalities, and conventional models still struggle with limited physical interpretability due to the single composition-property mappings. Here we develop a hybrid machine learning pipeline, incorporating LLM-based data mining and high-throughput CALPHAD, to predict the creep threshold stress (sigma th) of cast Al alloys with insight-encoded representations. Within the proposed Al-Ni-Er-Zr-Sc-Mn system, a family of candidate compositions is identified with predicted threshold stresses greater than 70 MPa at 573 K under the dislocation-climb mechanism. This 11 % improvement over the upper limit in our curated database results from three synergistic steps: high-fidelity meta-feature extraction, infusion of domain-informed descriptors, and explicit feature dimensionality reduction. Moreover, explicable strategies highlight interphase misfit strain, volume fraction of L12 precipitates, diffusional conductivity, and volume fraction of Al3Ni microfibers as four dominant contributors to thermodynamic and kinetic stability effects on the threshold. Leveraging these factors in assembling objective functions, composition screening via high-throughput CALPHAD converged on optima matching those from Bayesian optimization on the ML surrogate. The dual authentication between physics-aware and data-driven searches pioneers a reproducible pathway to efficiently develop creep-resistant lightweight alloys for next-generation propulsion and energy systems, where pistons and cylinder heads in diesel or downsized engines and related hot-zone castings require long-term dimensional stability.

Based on the rotary bending loading principle, an experimental investigation into the fatigue properties of 6201 aluminum alloy individual wires is presented. Considering the long design service life, high-cycle fatigue (HCF) behavior, and slender geometry (high length-to-diameter ratio) of these wires, a high-speed rotary bending fatigue test platform is custom-designed and constructed. The design mechanically maximizes the probability of fracture at the midpoint of the constant-cross-section specimen, even when considering clamping damage at the ends. Through mechanical derivation for the test platform, based on the beam bending theory and the finite element method, a quantitative relationship is established between the bending stress amplitude and the deflection angle at the clamped end. Using this platform, the fatigue lives of 6201 aluminum alloy individual wires under various bending stress amplitudes are tested, and the stress–life (S-N) curve is obtained. The developed experimental method and the reported fatigue data in this study provide essential experimental and material data for fatigue life prediction and engineering design of 6201 aluminum alloy individual wires.

Localized corrosion in multiphase aluminum alloys is governed by micro-galvanic coupling between intermetallic phases and the Al matrix. Here, an Al-Ce-Mg alloy containing an in-situ Al11Ce3 nano-network was fabricated via laser powder bed fusion to clarify the role of intermetallic morphology in corrosion behavior. Electrochemical measurements show that the as-built alloy exhibits an order-of-magnitude lower corrosion current density and higher polarization resistance than the heat-treated alloy. Scanning Kelvin probe force microscopy reveals that the nano-network reduces the potential difference between Al11Ce3 and the Al matrix from ∼111 mV to ∼26 mV. Meanwhile, XPS and TEM analyses reveal the formation of a compact CeO2-rich passive film that suppresses micro-galvanic corrosion and enhances corrosion resistance.

A comprehensive understanding of defect-microstructure-property relationships is essential for advancing additively manufactured aluminum alloys. In this study, an Al-9.5Ce-0.5Si-0.6 Mg alloy was fabricated by laser powder bed fusion (L-PBF). In-situ X-ray computed tomography (XCT), combined with multi-scale microstructural characterization, revealed distinct defect-dependent damage mechanisms. Irregular lack-of-fusion (LOF) pores generated severe localized stress concentrations and served as dominant crack initiation sites, whereas small spherical gas pores exhibited isolated volumetric growth with negligible contribution to catastrophic failure. By tailoring processing parameters, LOF porosity was effectively suppressed while promoting a microstructural transition from columnar to fine equiaxed grains, coupled with interconnected nanoscale alpha-Al + Al11Ce3 eutectic networks. In the absence of severe LOF defects, damage evolution is dictated by intrinsic microstructural heterogeneity, where coarser eutectic networks at melt pool boundaries (MPBs) drive pronounced strain localization and govern final failure. Benefiting from the synergy of LOF pore suppression and microstructural refinement, the optimized L-PBF Al-Ce-Si-Mg alloy achieves a superior strength-ductility balance, with a yield strength of similar to 285 MPa, an ultimate tensile strength of similar to 427 MPa, and an elongation to fracture of similar to 7%, significantly outperforming most reported L-PBF Al-Ce alloys. These findings clarify the roles of porosity and microstructural heterogeneity in damage evolution and provide mechanistic guidance for the design of high-performance additively manufactured aluminum alloys.

Producing high-conductivity aluminum conductors for power transmission involves 23 trace elements and multiple interconnected thermo-mechanical stages. The ultra-low alloying levels required to preserve high electrical conductivity create a narrow compositional window and highly imbalanced distributions, which hinder traditional data-driven learning. Here, we developed a physics-guided machine-learning framework based on 4458 valid industrial production records to predict tensile strength and electrical resistivity. In addition to raw composition and process parameters, we introduce ratio descriptors (e.g., Fe/Si and Al/Si) and propose a physics-informed metric termed the Equivalent Solute-Heat Index (ESHI) to couple key solute chemistry (Si, Fe, B) with normalized thermal-history intensity. Fe and Si primarily influence resistivity through impurity/solute scattering, while B mainly affects microstructural uniformity via grain refinement. Incorporating ESHI as an augmented signal into the best-performing XGB surrogate markedly improves generalizability, increasing the tensile strength R2 from 0.75 to ~0.92. SHAP analysis reveals that ESHI dominates the decision logic by modulating both targets with metallurgically interpretable mechanisms: solute-controlled scattering and thermal history-traced second-phase evolution that stabilizes the microstructure. NSGA-III was further employed to map the Pareto front and identify composition-process combinations that optimize the strength-conductivity trade-off, enabling improved mechanical reliability while minimizing resistive losses in practical power-transmission applications. Experimental validation on industrial wires confirms this reliability.

Preserving the mechanical properties of additive manufacturing Al-Fe alloys during thermal aging requires suppressing the decomposition of metastable nano-sized Al6Fe phase and preventing the formation of needle-like Al13Fe4 phase. This study employs first-principles calculations to investigate the effect of Mo doping on the interfacial properties of Al/Al6Fe and Al/ Al13Fe4, as well as Fe diffusion within the Al matrix. Analysis of four types of clean interface structures identifies Al (3 1 5)/Al6Fe (0 0 1) and Al (0 0 1)/Al13Fe4 (0 0 1) as the most stable orientations for their respective interfaces. Strong Al-Fe interactions at these interfaces result in Fe termination for all intermetallic phases, and the system achieves maximum stability when Mo substitutes surface Fe sites. Mo doping reduces the interface energy of Al/Al6Fe by 14.2 % while increasing that of Al/Al13Fe4 by 20.9 %, a contrasting effect due to a coordination number-dependent competition between bond-strengthening electronic effects and lattice-distorting geometric effects. Atomically resolved charge density difference and density of states analyses further elucidate the mechanism by which Mo modifies interfacial properties. Furthermore, Mo addition increases the diffusion energy barrier of Fe in the Al matrix by 20 %. This work simultaneously considered the thermodynamic and kinetic factors of phase precipitation, elucidating the microscopic mechanism by which Mo doping inhibits the phase transformation of Al-Fe alloys.

This study investigates the oxidation behavior and microstructural evolution of a Ni3Al-based IC-221 ​M alloy fabricated via laser-directed energy deposition (L-DED) during exposure at 800 ​°C in air. The alloy exhibits parabolic oxidation kinetics and significantly outperforms commercial 32Cr3Mo1V steel in oxidation resistance. Multiscale characterization reveals that a continuous Al2O3–Cr2O3 inner scale and a discontinuous outer NiO layer form rapidly, effectively inhibiting oxygen ingress. Initially, Zr-rich phases transform to ZrO2, providing a temporary diffusion barrier, but later develop cracks that accelerate oxidation. Long-term exposure leads to refinement of the L12 cellular structures and the precipitation of coherent (Ni, Cr)3(Cr, Al) phases in the face-centered cubic (FCC) matrix, which contribute to increased hardness. Thermodynamic modeling and diffusion simulations confirm the roles of phase composition and cation transport in scale formation. These insights advance the understanding of oxidation mechanisms in L-DED intermetallics and support the development of oxidation-resistant alloys for high-temperature applications.

Investigating the role of solid-solution rare earth elements (REs) in Al–Mg–Si alloys helps to gain a deeper understanding of the mechanisms of solid-solution elements, enabling the adjustment of alloy composition to improve the microstructure and overall performance of the alloy. This study employs first-principles computational methods to reveal the impact of rare earth element addition on the interface properties of α-Al/β″–Mg5Si6 and α-Al/β–Mg2Si in Al–Mg–Si alloys, as well as the effects on the mechanical properties of β″ and β precipitate phases. The study first constructs two interface models, Al(130)/Mg5Si6(100) and Al(001)/Mg2Si(001), to analyze their interface properties. Based on this, the substitution positions of rare earth elements in the interface models after their addition are further discussed. The results indicate that the atomic substitution positions of rare earth elements are related to crystal structure, interface properties, rare earth atomic radius, and electronegativity. Interface property studies show that the addition of rare earth elements significantly enhances the adhesion of Al(130)/Mg5Si6(100) and Al(001)/Mg2Si(001) interfaces, reduces interface energy, and strengthens interface stability. Additionally, the effects of rare earth element addition on the lattice mismatch of Al(130)/Mg5Si6(100) and Al(001)/Mg2Si(001) interfaces exhibit opposite trends and, to some extent, inhibit the β″ → β phase transformation. Mechanical property studies of the precipitate phases reveal that rare earth atoms in solid solution in β″ and β phases decrease the bulk modulus, shear modulus, and Young modulus, while increasing the Poisson ratio and B/G ratio. This indicates that the introduction of solid-solution rare earth elements reduces the alloy's stiffness and shear resistance while enhancing its plasticity and brittleness.

A novel Al-9.5Ce-0.6Mg/0.7GNPs (wt. %) composite with a network co-continuous Al/(Al, Mg)(11)Ce-3 eutectic structure was fabricated using laser powder bed fusion. Mg mainly exists in the Al11Ce3 phase rather than the Al matrix. The as-built composite achieved superior ultimate tensile strength (UTS) of similar to 452 +/- 3 MPa and an elongation of similar to 5.6 +/- 0.3 %. Heat treatment enhanced elongation (13.2 +/- 0.3 %) while maintaining high UTS (406 +/- 6 MPa), similar to 4 and similar to 5 times higher than as-cast Al-Ce alloy, respectively. High strength is ascribed to grain refinement, Orowan strengthening, and load transfer strengthening. The breakage of the (Al, Mg)(11)Ce-3 network was key to obtaining excellent ductility. The HT composite exhibits excellent corrosion resistance that is an order of magnitude higher than that of as-cast Al-Ce alloy in 3.5 wt % NaCl solution, Additionally, the galvanic corrosion between alpha-Al and (Al, Mg)(11)Ce-3 as well as the preferred corrosion of the melt pool boundary (MPB) was inhibited. This work provides a new idea for developing high strength-ductility synergy and corrosion resistance via additive manufacturing (AM) processing technique.

Quasi-in-situ tensile three-dimensional imaging was employed to systematically investigate the crack initiation and propagation mechanisms of IN718 alloy in large aerospace precision castings at different locations and after HIP treatment under room-temperature uniaxial tension. Particular emphasis was placed on the influence of the precipitate-micropore system, precipitate morphology, and their three-dimensional spatial distribution on crack evolution. The results indicate that rapid cooling promotes preferential crack initiation through network-like Laves phase and carbide precipitates, whereas slow cooling produces granular, discretely distributed precipitates that reduce local strain sensitivity. HIP treatment markedly decreases the Laves phase content, effectively suppressing crack initiation. Dislocations tend to accumulate at precipitate interfaces, inducing local stress concentrations and intra-precipitate cracking; micropores evolve into planar fracture surfaces under stress, and cracks may propagate along non-crystallographic paths due to branching or local stress perturbations. Crack propagation is strongly governed by elongated precipitates and element segregation networks. High-density microcracks with short nearest-neighbor distances substantially enhance crack coalescence, ultimately interacting with micropores to form the main crack. Quantitative analysis reveals that microcracks in regions with KDE > 14 and KNN < 0.1 exhibit the highest probability of coalescence, decisively influencing main crack paths. This study highlights the critical role of the precipitate-micropore spatial architecture in directing crack propagation and provides essential insights for predicting the fracture and failure behavior of IN718 alloy in key aerospace engine components.

Although significant progress has been made in the research of related technologies for metal porous membranes, their high rigidity and low flux still limit their widespread applications in gas-solid and liquid-solid separations. This work reported a flexible NiCu alloy porous paper membrane with controllable pore size and high flux achieved through the extreme Kirkendall effect. With the help of K2SO4, the Kirkendall effect between Ni powder and Cu foil is greatly enhanced through the path of surface diffusion. As a space-holder, the K2SO4 powder effectively reduces the shrinkage and densification of the Ni powders, ensuring sufficient surface diffusion channels, and promoting the formation of connected holes in the porous membrane skeleton. The results show that the uniformity of pore size distribution in the porous membranes can be precisely controlled by parameters including the sintering process, the initial composition of K2SO4, etc. Finally, a NiCu alloy porous paper membrane with a thickness of 60 mu m is obtained, in which the pore size and air permeability are analyzed to be 12.93 mu m and 1419.2 m3 center dot m-2 center dot kPa- 1 center dot h- 1 , respectively. In addition, the flexible NiCu alloy porous paper membrane shows good chemical stability in HF + H2SO4 mixed solution. And the membrane can efficiently separate the solid particles from the suspension with Al2O3 powder. This research provides a new approach and inspiration for the development of metal separation membranes applied in harsh environments.

Local microstructural inhomogeneities and casting defects often arise in complex thermal fields, and research how different cooling rates influence microstructures is key to realistic microstructure guidance. IN718 samples were prepared under 0.1 K/s, 1 K/s, 5 K/s, and 10 K/s to investigate this. Five metrics were chosen—secondary dendrite arm spacing (SDAS), grain size (GS), segregation degree (CSSD), microporosity neighbor count (MNC), and average microporosity volume (AMV)—to quantitatively analyze microstructure behavior. Nonlinear asymptotic functions were employed to describe the behaviors with respect to cooling rates ( x ) of microstructures: f_SDAS(x)=33.53x^-0.183 , f_GS(x)=60.361x^-0.397 , f_MNC(x)=0.9576x^0.308 , f_CSSD(x)=52.276x^0.032 , f_AMV(x)=3639.404x^-0.896 . The study clarified the microstructural coordinated behavior at different cooling rates and the correlation between properties and microstructural coordination, calculated the coupling relationships between various microstructural characteristics. A negative coupling relationship was identified between multi-element segregation and microporosity distribution with SDAS, grain size, and microporosity size, with the coupling strength ranked as: SDAS > grain size > microporosity size. A positive coupling was observed between micro-segregation and microporosity distribution, as well as among SDAS, grain size, and microporosity size. These findings provide valuable insights to study the effect of cooling on microstructure and help to optimization of cooling strategies during the manufacturing of IN718.

The surface of superalloy precision castings might exhibit defects after forming, posing a significant risk to their service life, necessitating inspection during post-process. Radiographic inspection, with its extensive research in automation, can achieve efficient and accurate detection of defects. However, it is limited in surface defects detection due to limited sensitivity to non-volumetric defects and high cost. In contrast, fluorescent penetrant inspection (FPI) is highly efficient for surface defect inspection due to its low cost, high sensitivity, and speed. However, manual examination introduces variability in the results, impacting the consistency and reliability of the inspection process. Automation is needed to ensure consistency and reliability of inspection. The implementation of an automated defect identification system based on FPI using convolutional neural networks (CNNs) was systematically investigated. Among the CNN models tested, MobileNetV2 exhibited exceptional performance, achieving a remarkable recall rate of 0.992 and an accuracy of 0.992. Additionally, the effect of class imbalance on model performance was carefully examined. Furthermore, the features extracted by the model were visualized using Grad-CAM to reveal the attention of the CNN model to the fluorescent display features of defects. This study underscores the strong capability of deep learning architectures in identifying defects of precision casting components, paving the way for the automation of the entire FPI process.

The advent of revolutionary advances in artificial intelligence (AI) has sparked significant interest among researchers across a spectrum of disciplines. Machine learning (ML) has become a potent tool for advancing materials research, offering solutions beyond traditional methods. This study discusses traditional machine learning (TML) and deep learning (DL) algorithms, providing a concise overview of commonly used ML algorithms in materials research. It also examines the general workflow of ML applications in superalloys, focusing on key aspects such as data preparation, feature engineering, model selection, and optimization, offering insights into the ML modeling process. From the perspective of the materials tetrahedron, this review explores ML applications in the research and development of superalloy composition, microstructure, processing, and performance. It highlights the use of advanced ML models to predict material properties, optimize alloy compositions and microstructure, and enhance manufacturing processes. It covers the use of advanced ML models and discusses the prospects of ML in superalloy research, highlighting its transformative potential in alloy material science.

The coarsening of strengthening phases, phase transformations, and interface stability between these phases and the matrix play a crucial role in determining the strength, ductility, and heat resistance of aluminum alloys fabricated through additive manufacturing (AM). In this study, a heat exposure experiment at 400 °C for 1 h was conducted on the laser powder bed fused Al–Ce–Mg alloy. A comprehensive analysis of the microstructural evolution, phase composition, interfacial bonding strength, and mechanical properties before and after heat exposure was performed using synchrotron X-ray diffraction techniques, first-principles calculations, transmission electron microscopy, and mechanical testing. For the first time, a phase transformation from Al11Ce3 to Al4Ce was discovered. Some semi-coherent relationships of [301]Al11Ce3//[011]Al, (060)Al11Ce3//(00)Al were transformed into coherent relationships of [001]Al4Ce//[001]Al, (200)Al4Ce//(00)Al, and the interfacial energy was reduced by 4.385 J m−2. In-situ Al11Ce3 nano-networks after heat exposure were retained and Al4Ce nanoparticles contribute to the thermal stability of the alloy by hindering dislocation motion and grain coarsening. The strength of the heat exposure alloy reached up to 416 MPa, which is about 95 % of the strength of the as-fabricated state and the elongation increased from 9.3 % to 13.8 %. These results provide a new approach for the design of high strength-ductility synergy and heat-resistant aluminum alloys via AM.

Accelerating the diffusion rate of Zr in aluminum and suppressing Zr segregation during solidification are crucial for enhancing the heat resistance of Al-Zr alloys. In this study, ab initio molecular dynamics simulations were employed to investigate the local structure of molten Al-Zr-Y(Si) alloys and the morphology of the liquid/substrate interface. By examining the atomic interactions, the mechanism by which Si addition improves Zr segregation during solidification and accelerates Zr diffusion was revealed. The results indicate that the interaction between Si and Zr is significantly stronger than that with other alloying elements such as Al, Y, and Zr itself. The introduction of Si had a discernible detrimental effect on Zr-Zr bonds, as Zr atoms exhibited a preference for bonding with Si atoms, consequently promoting cluster formation. This phenomenon resulted in the fragmentation of large Zr clusters into smaller ones. Moreover, the inclusion of Si atoms notably augmented the diffusion coefficient of Zr atoms within the Al-Zr-Y alloy. Analysis of the solid-liquid interface unveiled a noteworthy dragging effect, where Si atoms prominently pulled Zr atoms into the liquid phase at the interface's forefront.

Non-metallic inclusions are one of the common defects in stainless steel castings and have received widespread attention. In this paper, the morphology and composition of non-metallic inclusions in stainless steel investment casting were studied by metallography. The experimental results suggest that there are numerous non-metallic inclusions in this stainless steel casting. Due to the different formation mechanisms, these non-metallic inclusions vary greatly in morphology and chemical composition. The main inclusions are circular or spindle-shaped endogenous composite inclusions composed of a variety of oxides, whose substrate is manganese silicate (MnO-SiO2), rich in Al2O3, CaO, Cr2O3 and TiO2. A small number of inclusions are sickle-shaped exogenous inclusions produced by the furnace lining repair materials SiC-Al2O3 involved in the melt. The endogenous inclusions appeared as black granules with clear internal structure under optical microscopy (OM), with equivalent diameter distribution from 2 to 8 µm, and a few circular inclusions with sizes up to 80 µm. The endogenous inclusions are encapsulated in a niobium carbide shell, which was quite different from the exogenous inclusions. The exogenous inclusions are larger in size and aspect ratio and have multiple endogenous inclusions attached to the surface. The amount and size of coarse inclusions in stainless steel castings can be reduced by measures such as promptly keeping the furnace lining clean and sufficiently removing the steel slag from the melt.