SiC is a common reinforcement in Al matrix composites, which can improve the mechanical and physical properties. However, the widespread application of Al-SiC composites is limited by the poor wettability of SiC in molten Al. Herein, we investigated the underlying mechanism in A356/SiC composites synthesised by semi-solid stir casting. The microstructure at the interface between the Al matrix and SiC was characterised by SEM and TEM. Microstructural characterisation uniquely identified alumina films at the interface as the critical factor responsible for interfacial cracking and wettability inhibition. The wettability of SiC can be improved when SiC achieves intrinsic contact with Al by removing alumina film during collisions with SiC particles and primary α-Al. Strikingly, the operative wetting mechanism of SiC in molten Al is not lattice mismatch, but rather depends on the chemical nature of the SiC particle surface.
This work presents crystal plasticity finite element (CPFEM) simulations to investigate the grain-scale plastic deformation in an oligocrystalline tantalum specimen subjected to different strain levels, using initial crystallographic orientations derived from electron backscatter diffraction (EBSD) data. A dislocation density-based constitutive model considering the contributions of statistically stored dislocations (SSDs) and geometrically necessary dislocations (GNDs) is employed to describe the evolution of slip resistance during the deformation. The predicted stress–strain response shows reasonable agreement with literature-reported data. The simulations reveal pronounced accumulation of GNDs at grain boundaries, with density decreasing towards grain interiors and increasing systematically with strain. The influence of initial crystallographic orientation on deformation heterogeneity and texture evolution is also investigated and compared with reported data.
Island scanning is widely used in Laser-based Powder Bed Fusion; however, its influence on porosity formation compared with conventional cross-hatching has not been sufficiently investigated, particularly under varying hatch spacing. In this work, Ti6Al4V components were fabricated using crosshatch and island scanning strategies over a range of hatch spacing factors (A 1 = 0.5-0.9), intentionally covering a regime associated with lack-of-fusion defects. Porosity distribution and microstructures were characterised using optical microscopy, scanning electron microscopy, and X-ray computed tomography, while Vickers hardness testing evaluated the mechanical response to porosity. Both strategies exhibited characteristic necklace-like porosity near boundaries; however, island scanned components showed substantially higher overall porosity, approximately five times that of crosshatch components, due to persistent island-boundary-related pores. Minimum porosity occurred at A 1 = 0.6-0.7 for both strategies, whereas overheating-related irregular pores dominated at A1 = 0.5 and lack-of-fusion defects became prevalent at A 1 = 0.8-0.9. Island scanning also showed a higher probability of extra-large pores (>= 100 mu m), which were absent in crosshatch components. Hardness decreased by approximately 10-20 HV at higher A 1 values, consistent with increased defect content. Linear regression confirmed a negative correlation between porosity and hardness, with a stronger dependence for island scanning (R & sup2; = 0.69, p = 0.00012) than for crosshatch scanning (R & sup2; = 0.30, p = 0.034). Using the melt pool geometry criterion from literature, a target processing zone was identified, where a Tang-Pistorius-Beuth inequality range of 0.4-0.75 provides optimal conditions for defect reduction in crosshatch scanning, but does not apply to island scanning.
Laser-assisted robotic roller forming (LRRF) significantly improves the formability of ultra-high-strength steels (UHSS, >= 1.5 GPa) through localised thermal softening, yet predicting its transient thermo-mechanical response and springback remains fundamentally challenging. A critical domain gap exists between standardised isothermal calibrations and the highly dynamic, gradient-dominated in-process conditions, rendering conventional constitutive models and purely data-driven surrogates unreliable under distribution shift. This work presents a physics-informed hybrid machine learning framework for MS1700 martensitic steel (1700MPa grade) that explicitly bridges this gap. A two-level experimental dataset is constructed to explicitly link material-level thermo-mechanical behaviour with process-level state variables. Methodologically, a stacking ensemble leveraging Random Forest and Gradient Boosting is integrated with a physics-informed neural network (PINN). The PINN enforces experimentally validated gradient-sign constraints-thermal softening, early-strain hardening and energy-driven springback reduction-via an adaptive loss function that progressively balances data fidelity with physical consistency. The stacked model achieves R2 = 0.9996 (RMSE = 4.56 MPa) for stress prediction and R2 = 0.9539 (RMSE = 1.24 degrees) for pass-wise springback prediction. Crucially, compared to purely data-driven baselines, the physics-regularised framework successfully suppresses unphysical extrapolations in out-of-distribution evaluations, providing a robust, physically consistent digital-twin pathway for process optimisation and real-time compensation.
An accurate characterisation of irreversible hydrogen traps in steel is crucial in developing advanced materials that are resistant to hydrogen embrittlement. However, the hydrogen capture behaviour of manganese oxide (MnO) containing inclusions in steels and associated mechanisms are poorly understood. This study provides a systematic evaluation of hydrogen interaction with MnO-containing inclusions. The calculated hydrogen binding energy based on thermal desorption spectroscopy (TDS) is 160.64 kJ & centerdot;mol(-1), where scanning Kelvin probe force microscopy (SKPFM) has established a significantly higher micro-potential for the inclusion body relative to the matrix and the interface following hydrogen charging. The analysis has confirmed that hydrogen is preferentially captured and stored in the MnO-containing inclusions. This study is the first to unambiguously identify MnO-containing inclusions as effective irreversible hydrogen traps, providing an essential theoretical and experimental basis for enhancing the hydrogen resistance of steels by tailoring the inclusion characteristics.
Metallic glasses (MGs) exhibit superior engineering properties such as high strength and good corrosion resistance but often suffer from limited plasticity. The present study investigates the temperature rise within shear bands, as the primary deformation mechanism and its influence on plasticity. The shear bands are modelled as instantaneous planar heat sources, enabling the calculation of thermal profiles that show a peak temperature rise at the centre of shear bands. Correlation has been observed between such temperature rise and the liquid fragility index (m) (ranging from 16 to 200) and the fragility parameter (D*) (between 10 and 26). Furthermore, the bulk-to-shear modulus ratio (K/G) is examined in relation to the fragility index. These findings highlight the role of localised heating within shear bands and its dependence on the glass fragility, offering insights into enhancing plasticity in MGs.
Three-dimensional (3D) nanoarchitectures are emerging as key platforms for high-density data storage, high-speed information processing, and charge-free logic operations in spintronic and magnonic technologies. Within this landscape, 3D artificial spin ice (3D-ASI) systems are particularly important because their highly frustrated nanomagnet arrangements give rise to complex magnetic charge states that cannot be achieved in planar structures. Their intricate 3D interactions, curvature- and shape-induced effects, unconventional switching pathways, and emergent phenomena make them highly promising for neuromorphic computing, reconfigurable magnonics, and other next-generation applications. In this work, we use micromagnetic simulations to explore the direction-dependent magnetisation switching in a 3D network of magnetic nanowires forming an icosahedral nanoarchitecture. Our results reveal a previously unexplored 3D-ASI behaviour in this geometry, where the magnetisation reversal proceeds through a devil's staircase-like sequence of discrete plateaus. The detailed examination of the magnetic microstate at each plateau of the hysteresis curve reveals a unified mechanism governing the stabilisation and transport of high-magnetic-charge vertices (Q = +/- 3Q or +/- 5Q), which are energetically expensive. These findings position the icosahedral 3D-ASI as a promising platform for neuromorphic computing and sustainable reconfigurable magnonic technologies.
Fatigue crack propagation tests were carried out on Nimonic 80A electron beam welded joints with different stress ratio using standard CT specimens. The crack propagation rate was characterised using the ‘[Formula: see text]’ method, the ‘[Formula: see text]’ method and the ‘[Formula: see text]’ two-parameter method. Subsequently, a new crack propagation rate model is proposed, and its accuracy is verified by co-simulation using Abaqus and Franc 3D. The new model can better describe the crack propagation rate under different stress ratio.
This current study emphasises understanding the influence of the coating thickness and powder feed rate on microstructural evolution and their subsequent effects on electrochemical and wear behaviour of the Fe-based amorphous/nanocrystalline composite coating (Fe57Cr9Mo5B16C7P6, at. %) synthesised via high velocity oxy fuel (HVOF) spraying. Microstructural analysis revealed that with an increase in the coating thickness, porosity content decreased with improved intersplat bonding; whereas the amorphous phase fraction reduced. Regardless, with an increase in the powder feed rate in both low and high thickness coatings, amorphicity increased with reduced porosity content, resulting in dense coating microstructure. Both micro and nanohardness increased with an increase in coating thickness due to a higher proportion of crystalline phase fraction and low porosity content whereas an increase in overall hardness with an increase in powder feed rate is attributed to low porosity content in the coatings. Wear test in the coating with high thickness (400 +/- 20 mu m) and high powder feed rate (60 gmin-1) exhibited superior wear resistance. This was attributed to a low porosity fraction and a high proportion of intermetallic phases. Potentiodynamic polarisation test performed in 3.5% NaCl solution showed that coating with high thickness (400 +/- 20 mu m) and high powder feed rate (60 gmin-1) exhibits superior corrosion resistance attributing to dominance of the optimum level of reduced porosity content over amorphous phase fraction.
A strategy is proposed to enhance the mechanical properties of metallic glasses using multilayered composites with various initial free-volume gradient interfaces and validated by finite element modelling. We found that the ductility of the composites improves significantly with the increasing number of layers. The main factors and the underlying mechanisms are (a) the gradient interface with varying free volume densities that can reduce the local stress concentration, (b) size effects imposed by the layer thickness that limits the local shear and shear bands to grow critically longer and thicker to cause catastrophic failure, (c) the presence of interface barriers to increase the probability of blocking and retarding the shear banding, and (d) the heterogeneity introduced by the statistical distribution of free volumes. The results demonstrate that the multilayered composites are promising in solving the strength-ductility tradeoff in metallic glasses.
Emerging biomedical devices like implantable microsystems present new assembly and packaging challenges due to their miniaturization, complex integration, and diverse material combinations. Metals, glass, ceramics, and polymers are frequently used in biocompatible devices; reliably joining these dissimilar materials remains a critical concern. Researchers are working on different dissimilar combinations of metals to meet the growing demands of metallic biocompatible implants in the biomedical sector. The conventional adhesive bonding often fails to ensure long-term biocompatibility, it necessitates alternative joining techniques. Laser joining has emerged as a promising solution for achieving hermetic sealing, owing to its non-contact nature, localized heat input, and ability to preserve delicate biomedical components. Few studies are available on the joining of dissimilar metals, focusing on biomedical applications. The present study makes a comprehensive investigation of laser joining of titanium alloys to steel specifically for biomedical applications - an area with limited existing research. This study provides insight into the evolution of welding defects, strategies for their mitigation, and the underlying metallurgical transformations during laser joining. Special attention is given to how intermetallic phases form and spread at the interface, how they affect joint strength, and how they relate to laser process parameters. Furthermore, the review advances current understanding by systematically analyzing weld microstructure, geometry, and mechanical performance, addressing key gaps in existing literature. This review also highlights emerging trends, challenges, and future research directions for enabling more reliable and biocompatible joining of dissimilar materials for the future.
Powder-based additive manufacturing (AM) of commercially pure titanium (CPTi) presents challenges regarding feedstock handling, surface oxide formation, and limited build volume. This study examines the microstructural, mechanical, and crystallographic properties of wire arc-directed energy deposition (WA-DED) CPTi specimens utilising a novel localised shielding technique. Implementing a high-purity argon purging system effectively mitigated oxide formation during the printing process. X-ray diffraction analysis revealed a preferential crystallographic orientation along the (002) plane, with a consistent alpha-titanium phase. Residual stress measurements significantly reduced from 203.53 MPa in as-printed specimens to 17.02 MPa after heat treatment. Microstructural examination showed serrated and acicular alpha-grains with an average size of 22 mu m. Microhardness and tensile testing revealed mechanical properties comparable to those of Grade 3-Ti, with a ductile failure mechanism. The research highlights the potential of the advanced localised shielding technique in enhancing the quality of large-scale additively manufactured titanium components.
This review article critically examines the influence of alloying elements, namely silicon (Si), tin (Sn), strontium (Sr), zinc (Zn), and zirconium (Zr), on the microstructures, mechanical properties, and corrosion resistance of magnesium alloys, with the primary focus being on biomedical applications. Magnesium alloys have garnered significant attention owing to their lightweight nature and potential for enhancing product performance. This review systematically explores how these alloying elements impact mechanical properties, microstructural configurations, and corrosion resistance, shedding light on the intricate mechanisms governing these changes. Emphasis is placed on their role in improving tensile strength, hardness, and ductility while balancing corrosion resistance to meet industrial demands. Additionally, considering the growing interest in magnesium alloys for biomedical applications, this review briefly highlights their potential for biodegradable implants, where tailored alloy compositions can optimize biocompatibility and degradation rates. A comprehensive synthesis of existing research findings offers valuable insights for researchers, engineers, and practitioners in optimizing magnesium alloys for biomedical applications.
The purpose of this study is to evaluate the effectiveness of various machine learning algorithms in predicting the ultimate tensile strength (UTS) of friction stir welded joints. This prediction is crucial for assessing weld quality and integrity. Several investigations are carried out in linear and non-linear regression models, including Poisson Regressor, Gradient Boosting Regressor, Bayesian Ridge, k-Nearest Neighbours, Lasso, Random Forest, Elastic-Net, and Support Vector Regression, using datasets of welding parameters. The models were evaluated for their prediction accuracy and reliability using mean absolute error (MAE), mean square error (MSE) and R2 score metrics. The findings revealed significant variations in model performance. Non-linear models, especially Decision Trees and k-Nearest Neighbours, have exhibited the highest efficiency. The Decision Tree model achieved a remarkable R2 score of 0.97. Meanwhile, the k-Nearest Neighbours model noted the lowest MSE value of 408.62, whereas the MAE value was 14.75. In comparison, the Support Vector Regression model lagged significantly, registering the best MSE and MAE of 14824.74 and 107.73, along the side of a negative R2 score of −0.22, indicating deficient performance. This study highlights the potential of machine learning to forecast the quality of the weld joints. It proposes that the utilisation of these models can significantly enhance production processes and optimise material performance in industrial applications. The study demonstrates the integration of machine learning into manufacturing workflows to enable predictive maintenance and enhance quality control.
Thermoelastic stresses induced in filament wound composite pressure vessels by temperature changes significantly influence the resulting load of the pressure vessel. This study presents the analytical approaches based on classical lamination theory and netting theory, which can be used for computing these thermoelastic stresses in the basic parts of the pressure vessel, such as the cylinder part, the end dome, and the junction area between them. Two material configurations were considered - glass-epoxy and carbon-epoxy. Concerning the knowledge of the thermoelastic stresses in analysed essential parts of the vessel, the Hoffman failure index analysis was performed. Using this failure index analysis, the critical places of the pressure vessel can be easily detected. Using data-driven evolutionary algorithms, the invariance of the pressure vessel geometry concerning the thermoelastic stresses was verified. Knowing the critical places of the pressure vessel may improve the designer's decision during the development and design process.
The fishscaling resistance of enameled steel is closely related to the hydrogen-related behaviour of its constituent phases, in which dispersed co-precipitates are regarded as the most effective hydrogen-trapping sites. To investigate the respective roles of the individual precipitates on the hydrogen trapping ability and locations in enameled steels containing a co-precipitate, in situ Scanning Electron Microscopy (SEM), correlated with Scanning Kelvin Probe Force Microcopy (SKPFM), was employed in this work. Chemical analysis from Energy Dispersive Spectrometer (EDS) mapping showed that the co-precipitate was composed of four precipitates, namely Ti4C2S2, Ti(C,N), TiN, and Al2O3. Changes in surface micro-potentials before and after hydrogen charging were measured for each single precipitate using SKPFM technology. There existed a significant difference in the potential change between the four composite precipitates, which could distinctly reflect the difference in hydrogen trapping abilities and fishscaling resistance. The results showed that the hydrogen trapping abilities of the four precipitates were in the following order: Ti(C,N)>Al2O3>Ti4C2S2>TiN. Furthermore, a peak analysis of the potential demonstrated that the hydrogen atoms were mainly trapped in the interface zone of the Ti4C2S2 and TiN precipitates, while the dominant hydrogen trapping locations of Ti(C,N) and Al2O3 precipitates were primarily the centre area of the particles.
This work aims at determining the effect that variations in material data have on predictions of growth morphologies for the Ag-Cu system. The predictions are based on the Kurz-Giovanola-Trivedi (KGT) model, which establishes quantitative relations between the solid-liquid interface velocity, alloy composition, and interface temperature. These relations can be used to predict the growth morphology for constrained growth conditions. Material data were extracted from the available literature to calculate the morphology changes for Ag-5wt% Cu. When considering the range of reported values of this alloy for the interface energy, melting entropy, liquid diffusion coefficient, and the characteristic system length, the transition from cellular to plane-front growth at high solidification velocities – commonly denoted as the absolute stability limit – could occur as low as 0.07 m/s or as high as 1.17 m/s. These predictions based on the KGT model are combined with experimental laser glazing of arc-melted buttons to identify melt-pool microstructures. Button cross-sections were glazed at 400 W with scan speeds of 0.1–0.3 m/s to demonstrate the accuracy of the model. The resulting microstructures are analysed from cross-sections using scanning electron microscopy. The predictions approximately match experimental observations of the absolute stability limit when median material property values are used. It is concluded that the large range of predicted laser velocities for absolute stability suggest that new approaches will be required if the analytical models should help guide additive manufacturing processing. In the meantime, the models are useful for predicting trends, for example, for alloy- or additive manufacturing technology selection.
In this work, we employed in-situ S/TEM techniques to investigate deformation and damage mechanisms in neutron-irradiated reduced-activation ferritic/martensitic (RAFM) EUROFER97 steel subjected to a dose of 15 dpa at 330 degrees C. The irradiated microstructure revealed uniformly distributed dislocation loops and sporadic nanometer-sized cavities. During in-situ straining of a focused-ion beam (FIB)-prepared lamella, mobile line dislocations were observed to interact with dislocation loops, leading to loop absorption and the formation of dislocation networks. Further straining resulted in the formation of a dislocation-loop-free zone. Within this region, deformation-induced nanometer-sized cavities emerged, likely from pre-existing irradiation-induced clusters, cavities, or remnants of absorbed loops. Coalescence of these cavities led to the formation of micro-cracks and ultimately to the fracture of the lamella. This suggests premature failure of the irradiated sample compared to its unirradiated counterpart, highlighting irradiation-induced embrittlement.
The Aluminum Conductor Composite Core (ACCC), used in overhead transmission lines, possesses a range of exceptional properties such as high strength, light weight, corrosion resistance, high-temperature resistance, and low sag. In order to address the technical challenges associated with poor bending performance and the lack of effective means for inspection of hidden defects in the composite core, an aluminum conductor with an optical fibre multi-strand carbon fibre composite core has been developed and subjected to performance testing. The results demonstrate that, compared to the single-core conductor, the optical fibre multistrand-core conductor exhibits characteristics such as low density, high tensile strength, excellent bending performance, and a high safety factor. Notably, the multi-strand core boasts a tensile strength of 2980 MPa and a bending performance of 25 D (where D is the diameter of the core). In addition, the optical fibre embedded in the strands of the core can be used as a sensor to detect the hidden defects, thus presenting significant potential for widespread application.
In the quest for understanding and tailoring the formation and properties of entropically stabilised ceramics, rocksalt-structured (MgNiCoCuZn)O occupies a central place. To date, most of the reported high-entropy rocksalt oxides (HERSOs) are derivatives of the original (MgNiCoCuZn)O, and only a couple of other novel HERSOs have been synthesised. To pave the way for the discovery of new HERSOs, we seek rapid and effective methodologies for screening large compositional spaces such as those associate with high entropy oxides. In this letter, we analyse modifications of the Hume-Rothery (HR) rules that govern the formation of solid-solution metallic alloys. We propose that HERSOs can form based on maintaining an oxidation state of +2 for all cations and a low value for the coefficient of variation associated with the lattice constants of the unmixed constituent oxides in a (hypothetical or actual) rocksalt phase. As a result of applying Hume-Rothery-inspired rules, we provide an heuristic explanation for the synthesizability of three experimentally realised HERSOs, and list a significant number of other HERSO compositions that can potentially be realised via similar methodologies. The rules devised in this work for HERSOs should provide guidance for future synthesis efforts, and we expect that community use could lead to further refinements as well as to modifications for application-specific screening.
