In this work, D2205 duplex stainless steel has been laser shock peened without an ablative layer. The effect of shock peening on the microstructure, tensile strength, wettability, protein adsorption and biocompatibility has been studied. Due to thermal effect of the high-energy pulsed laser used for peening, lattice micro-strain and dislocation density decreased, and lattice parameters and grain size increased in the vicinity of the shock peened surface. However, beneath the heat affected area, the nature of residual stress changed from compressive in ferrite and tensile in austenite to compressive for both. Within 1 mm distance from the shock peened surface, induced residual compressive stress and grain refinement led to increase in nano-hardness from similar to 3.8 GPa to similar to 4.3 GPa. In addition, the yield and ultimate tensile strengths increased to 709 MPa and 845 MPa, respectively on the shock peened surface compared to 611 MPa and 750 MPa, respectively on the unpeened side. Laser shock peening led to decrease in surface energy and increase in hydrophobicity, indicated by increase in contact angle in the sessile drop test. Protein adsorption got decreased on the shock peened surface due to increase in hydrophobicity. Cell proliferation and decrease in secretion of pro-inflammatory cytokines indicated better biocompatibility.

Fusion-based additive manufacturing of high strength 2024 alloy is frequently limited by coarse columnar grains, microsegregation, and solidification defects, which jointly prevent the balance between strength and toughness. To address these limitations, a hybrid additive manufacturing strategy integrating directed energy deposition (DED) with cyclic interlayer friction stir processing (FSP) was proposed in this work. The hybrid process generated a multizone heterogeneous microstructure consisting of deposition zones, remelt zones, nugget zones, and thermomechanically affected zones, with spatial variations in grain structure, recrystallization behavior, dislocation density, texture, and precipitation state. Severe plastic deformation during FSP transformed the deposited coarse grains into ultrafine equiaxed grains in the nugget zone, where the average grain size was refined to 2.34 mu m, while cyclic reheating during subsequent deposition promoted non-uniform precipitate redistribution and microstructural transitions across adjacent regions. Compared with the as-deposited DED alloy, the hybrid-built material exhibited markedly improved tensile properties, with a yield strength of 272 MPa, an ultimate tensile strength of 349 MPa, and an elongation of 6.3%, along with reduced anisotropy. The property enhancement is associated with the synergistic contributions of grain-boundary strengthening, defect elimination, precipitation regulation, and hetero-deformation-induced back-stress strengthening enabled by the layered heterogeneous architecture. The present study provides mechanistic insight into heterostructure evolution under coupled thermo-mechanical conditions and offers an effective route for improving the strength-ductility balance of additive manufacturing precipitation-strengthened aluminum alloys.

The extremely rapid cooling rates inherent to laser powder bed fusion (LPBF) processing produce unique microstructural features. In LPBF-fabricated 316 L stainless steel (SS316L), the formation of metastable sub-grain structures enriched with dislocations has been directly associated with superior tensile performance at ambient conditions. However, the absence or instability of these substructures at elevated temperatures can significantly alter mechanical behaviour. In this work, the high-temperature deformation response of LPBF-fabricated SS316L is systematically investigated, with particular emphasis on the effects of build orientation and strain rate. Uniaxial tensile tests were conducted along both the vertical (along the build direction) and horizontal (perpendicular to the build direction) orientations at a temperature of 850 degrees C under multiple strain rates. At room temperature, plasticity was primarily accommodated by dislocation slip and deformation twinning, whereas at elevated temperatures, a transition was observed, with dynamic recrystallisation becoming the dominant mechanism, especially in horizontally built specimens. However, the vertical samples retained elongated grains and exhibited comparatively limited recrystallisation. The tensile response demonstrated strong dependence on both strain rate and build orientation. The experimental findings, corroborated by crystal plasticity modelling, provide important insights into the orientation-and temperature-dependent plasticity mechanisms of LPBF SS316L. The applied model is capable of accurately predicting the high-temperature mechanical response by incorporating various temperature-dependent mechanisms.

Heat-treatable 7000 series aluminium alloys are high-strength, lightweight materials suitable for aerospace and automotive structural components. The 7000 series alloys have a tendency to solidification cracking during arc welding. Ceramic nanoparticles have recently emerged as an effective approach for enhancing weld integrity by modifying solidification behaviour. In the present study, two variants of aluminium alloy 7075 nanocomposite filler rods containing TiO2 and CeO2 nanoparticles were fabricated through ultrasonically assisted stir casting followed by hot extrusion. Gas tungsten arc-welded joints using extruded fillers were evaluated and compared with welds made using commercially available ER5356 filler. The effect of nano-treated fillers on microstructural features of welds was investigated using optical microscopy, field-emission electron microscopy with energy-dispersive spectroscopy, synchrotron X-ray diffraction, and electron backscatter diffraction. Furthermore, the strength of AA7075 welds was investigated through microhardness and tensile tests after post-weld heat treatment. The nanoparticle-added filler provided crack-free welds with a comparatively refined microstructure. Mechanical characterization demonstrated a significant improvement in weld strength with nanoparticle addition. The weld produced with ER5356 filler exhibited an ultimate tensile strength (UTS) of approximately 298 MPa and 3.5% elongation. The filler containing CeO2 exhibited the best overall performance with a UTS of approximately 498 MPa and elongation of ∼7.5%, corresponding to a weld joint efficiency approaching 0.88 relative to the AA7075-T6 base metal. Overall, the study demonstrates the effectiveness of nanoparticle-modified filler rods in improving weld quality and mitigating cracking in high-strength AA7075 welds.

Creep-fatigue interaction in single-crystal nickel superalloys is difficult to predict because the response depends on the combined effects of loading parameters, hold time, temperature, and the underlying deformation mechanisms. This is important for turbine blade applications, where components experience both fatigue and creep during service. In the present work, a crystal plasticity finite element (CPFE) framework is used to study the creep-fatigue response of a single-crystal nickel superalloy under a range of practically relevant thermo-mechanical loading conditions. In particular, the effects of strain amplitude, R-ratio, hold duration, and temperature on cyclic deformation, stress relaxation, damage evolution, and creep-fatigue life are examined. Particular attention is given to separate the roles of fatigue and creep damage, understanding their interaction, and identify the creep-dominated and fatigue-dominated regimes as a function of strain amplitude and hold time. The study brings together these effects within a single framework and shows that the predicted trends in cyclic response and life are in good agreement with experimental observations reported in the literature.

This study employs machine learning (ML) models such as Decision Tree (DT), Extra Tree (ET), K-Nearest Neighbors (KNN), Support Vector Regressor (SVR) and Lasso to predict how Ta and Nb content effects the mechanical behavior of the CoCrFeNi HEAs at diverse compositions. ET and KNN models developed as the most effective forecasters, accomplishing high R2 values of 0.985 and 0.984 respectively. The valuation of the mechanical behaviour with experimental data is very precise with the help of these models. The advanced methods like Hybrid Computational Variable Creation Method (HCVCM), Symbolic Variable Creation Method (SVCM), Data Structure Method (DSM) was further used which has enhance the prediction performance in terms of R2 value upto 0.991 for ET and 0.99 for KNN model. A novel stress-strain behaviour was produced for a new composition with novel Ta and Nb content, where ET and KNN demonstrated robust R2 values of 0.984 and 0.98, respectively. The strategy streamlines exploration of HEAs by cutting down extensive trial-and-error experiments, thereby conserving significant resources in terms of time, cost, and energy.

Cold spray additive manufacturing (CSAM) offers a solid-state route to fabricate metallic structures with tailored porosity and hence stiffness for implant applications. Such engineered porous network allows integrating secondary phases with antimicrobial functionality into these structures while preserving bone-compatible mechanical behaviour. By careful process optimisation, a porous Ti6Al4V (Ti64) alloy structure has been fabricated by CSAM, followed by Ag infiltration into the interconnected pores, resulting in a composite structure with improved antimicrobial properties. The cold-sprayed porous Ti64 alloy exhibited compressive strength and elastic modulus of 281 f 16 MPa and 24.7 f 0.4 GPa, respectively. The strength exceeded the strength of cortical bone while closely matching with bone stiffness. Ag infiltration increased compressive strength to 305 f 9 MPa without significantly altering elastic modulus (22.4 f 0.3 GPa). Porous structures improved wettability and protein adsorption, while Ag infiltration retained favourable protein adsorption behaviour and provided sustained antibacterial efficacy against Staphylococcus aureus. This work demonstrates a scalable integration of processing, structure and function, enabling a mechanically compliant, antimicrobial porous Ti64 alloy infiltrated with Ag for load-bearing implant applications.

This study employs six different machine learning models to predict how the variation of an alloying element influences the magnetic behavior of the CoFeNiAlx High-Entropy Alloys (HEAs). Both XGB and KNN models developed as the most effective predictors, achieving R2 values of 0.997. These models precisely estimated the saturation magnetization in close alignment with experimental data. A new magnetization (M-H) curve was generated for a new composition, where XGB and KNN also demonstrated robust R2 values of 0.992 and 0.971, respectively. This method proposals significant time, cost, and energy savings by minimizing the requirement for extensive experimentation, progressing HEA research meaningfully.

The development of nano-treated filler materials has gained importance to achieve crack-free welds in joining heat-treatable aluminium alloy 7075 sheets, which are highly susceptible to solidification cracking during arc welding. For producing high-quality filler materials with minimal extrusion load, optimizing die design and process variables is essential. In the present work, a finite-element-based thermo-mechanical simulation is employed to investigate the influence of extrusion ratio, die angle, and billet temperature on the extrusion load, undersurface flow lines, and effective stress distribution. The study focuses on identifying the optimum combination of these parameters to minimize peak extrusion load, limit excessive stress concentrations to prevent damage, and ensure uniform, smooth material flow through the die exit. Finite element simulations have been carried out for the hot extrusion of AA7075 billets, and the optimized parameters were subsequently employed to produce nano-treated filler wire. The simulations revealed that the extrusion ratio and billet temperature were the significant parameters influencing the load. The peak extrusion load was found to vary from approximately 17 tons to 65 tons across the investigated parameter range, indicating a significant reduction of approximately 74

This study explores the mechanical and tribological behavior of IN625 and IN718 coatings deposited on Ni-based IN718 alloy substrates using the high-velocity air fuel, HVAF technique. Microstructural analysis revealed that the IN625 coating exhibited more visible splats, weaker bonding, and a greater presence of unmelted and partially melted regions than IN718. Both IN625 and IN718 coatings retained the original constituent phases from the powder. The IN718 coating, however, demonstrated superior mechanical properties, with its hardness and adhesion strength surpassing those of IN625 by 56

In recent years, high‐entropy alloys (HEAs) are attracting significant attention owing to their distinctive design adaptability and exceptional properties. Herein, machine learning methods namely extra tree (ET), K‐nearest neighbors (KNN), random forest (RF), support vector regressor, and linear regression are utilized to predict the mechanical properties of MoNbTaTiVAlx refractory HEAs across varying compositions and temperatures. By doing so, the study aims to minimize the dependence on experimental testing. Among the models, ET, RF, and KNN exhibit superior predictive performance, achieving R 2 values of 0.998 which closely align with experimental results. Additionally, a new stress–strain curve is generated for an aluminium composition of 0.4, with the ET, RF, and KNN models maintaining high predictive accuracy with R 2 values of 0.985, 0.978, and 0.97, respectively. This innovative application of machine learning significantly reduces the need for exhaustive experimental testing, resulting in considerable savings in resources and accelerating advancements in HEA research and development.

316L stainless steel (SS316L) is widely utilised due to its excellent corrosion resistance. However, its yield strength is relatively low, ranging from 170 to 200 MPa, which limits its engineering applications as a structural material. Reinforcing the metal with ceramic nanoparticles has been considered an effective approach to improve the strength of SS316L. However, the synthesis of such nanocomposites has been a long-term challenge with the conventional casting process due to the high tendency of agglomeration of nanoparticles. Laser powder bed fusion (L-PBF) additive manufacturing (AM) provides an opportunity to overcome this issue for fabricating stainless nanocomposites by moderating the powder constitution. This study demonstrates the preparation of a dense SS316L matrix TiN-WC nanoparticles reinforced nanocomposite with enhanced strength via L-PBF. Materials characterisation indicates the distribution and dispersion homogeneities of the TiN-WC nanoparticles in the SS316L matrix. A thermal computational fluid dynamics model explains the melt pool dynamics and temperature distribution of the composite powder bed. The yield strength, ultimate tensile strength, and elongation to fracture of the nanocomposites are over 700 MPa, 1000 MPa, and 30

In this work, we have attempted to predict the mechanical behaviour of light weight Mg-based rare earth alloys fabricated through different mechanical and thermal processes. Our approach involves machine learning techniques across a range of different thermomechanical processes such as solution treatment, homogenization, extrusion and aging behaviour. The effectiveness of machine learning models is evaluated using performance metrics, including Coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). After modeling and selection of best model, the mechanical behaviour of new alloys was predicted in terms of ultimate tensile strength, yield strength and total elongation. The predicted results highlight the superior predictive accuracy of the K-Nearest Neighbors (KNN) machine learning model, demonstrating its better performance metrics compared with other machine learning approaches. This model has been found to predict the material properties with an effective evaluation matrix (R2 = 0.955, MAE = 3.4% and RMSE = 4.5%).

This work examines the effects of prolonged heat‐treatment on the microstructure and mechanical characteristics of cold spray (CS) IN718. The heat treatment reduced coating porosity from 3% to 0.8%. The ageing treatment results in a synergistic effect of precipitation hardening from intermetallic phases and carbides, with a simultaneous reduction in the work‐hardened microstructure through recovery, manifested as reduced dislocation density in the coating. The as‐sprayed coatings exhibit reduced cohesive strength and toughness, along with a significant anisotropy in tensile characteristics, which is a primary objective of the study. The causes of the mechanical anisotropy in the CS coatings have been elucidated. The postprocessing resulted in an increase of ≈120% in cohesive strength and ≈75% in ductility of the coating due to the aforementioned alterations in microstructures. The tensile response of the coatings in both as‐sprayed and heat‐treated conditions has been analysed using numerical modelling, which utilizes crack widths and their densities within the coating. The model clearly shows that cracks perpendicular to the loading affect tensile strength. The model agrees with experimental tensile strength data and accurately predicts crack widths and densities in each anisotropic direction. The finite element models reveal an empirical connection between tensile strength and crack size.

In this study, we introduce a machine learning methodology for predicting mechanical properties in Mg-based multicomponent alloys, incorporating alloying elements and processing methods as input variables. We employed eight ML models and assessed their efficacy using metrics such as R2, RMSE, and MAE after using fivefold cross validation grid search hyperparameters tuning process. Following this, we implemented the topperforming Extra Tree (ET), Random Forest (RF) and XGB models to predict mechanical properties for Mg alloys. The best performance was observed with an R2 of 95.1 % and 97.2 %, RMSE of 7 % and 8 %, and MAE of 4.7 % and 5 % for UTS and YS respectively using the Extra Tree model. This research not only showcases the efficiency of ML techniques with minimal intervention but also offers valuable insights paving the way for accelerated design of Mg-alloys for tailored application in the future.

High entropy alloys (HEAs) have recently gained popularity due to their vast design possibilities and exceptional properties, offering a broad spectrum of mechanical and magnetic properties by combining various elements. This study employs machine learning models such as Extra Tree (ET), CatBoost (CB), Decision Tree (DT), KNearest Neighbors (KNN) and Support Vector Regressor (SVR) to predict how aluminium content influences the magnetic behavior of the homogenized CoCrFeNiAlx HEAs at different temperatures. Both ET and CB models emerged as the most effective predictors, achieving high R2 values of 0.992 and 0.989 respectively. The estimation of the saturation magnetization in close alignment with experimental data is very accurate using these models. A novel magnetization (M - H) curve was generated for a new composition at different temperatures, where ET and CB demonstrated robust R2 values of 0.982 and 0.952, respectively. This method offers significant time, cost, and energy savings by minimizing the need for trialing with the extensive experimentation n HEA's vast composition space.

Tailoring the strength and ductility of metals through microstructural design has been a longstanding endeavour. Here, we report the development of a hierarchical ultrafine twinned microstructure in zirconium via a novel multi-axial cryo-forging (MACF) process. MACF of Zr resulted in the formation of dense and fine tensile twins, {10 (1) over bar2}<10<(1)over bar>1> (T-1) and {11 (2) over bar1}<(11) over bar 26 > (T-2) within coarse and equiaxed grains. This microstructure exhibited a remarkable combination of enhanced strength, strain-hardening rate, and ductility, compared to its coarse-grained counterpart. The yield strength and ultimate tensile strength increased by up to 26 % and 30 %, respectively, without compromising material ductility. The improved strength and strain-hardening stemmed from the activation of the harder slip system and its consequent interaction with the fine twin boundaries. Notably, the growth of fine T-2 twins during ambient temperature deformation, absent in coarse-grained zirconium, contributed to additional ductility. Crystal plasticity simulations reveal that the activity of pyramidal slip within T-2 twins is twice that of prismatic slip, leading to an enhanced strain-hardening rate contributed by the T-2 twins. Subsequent heat treatment of MACF Zr at 500 degrees C relieved the elastic distortions around twin boundaries, and hence tensile straining led to stress-assisted easier migration of pre-existing twin boundaries. This resulted in a lowering of the strain-hardening rates. It was shown that the dislocation substructure around these typically incoherent tensile twins in hcp metals plays a crucial role in their strain-hardening behaviour.

NiTi alloys fabricated via additive manufacturing (AM) often suffer from coarse grains, brittle intermetallic phase accumulation, and limited control over phase transformation behavior, resulting in compromised performance and impeded functional applications. To address this challenge, a generalisable strategy for intermetallic modulation and functional gradient design has been proposed and validated through directed energy deposition (DED). By employing multiple deposition modes (Mixed NiTi, Graded Ti/Ni, and Graded Ti/NiTi), tailored microstructure gradients were achieved. This approach enabled spatial control over the formation of key intermetallics, resulting in simultaneous enhancement of martensitic transformation behavior and mechanical performance (nanohardness, compressive strength). A coupled simulation-experimental analysis revealed universal mechanisms of temperature evolution and solute transport in melt pools, which underlie intermetallic development during AM. The findings contribute a broadly applicable methodology for designing gradient architectures in metallic systems, offering new avenues for tailoring functional and structural performance.

This study investigates the effects of high-velocity oxy-acetylene flame treatment (FT) and along with high pressure (4 MPa) air jet compression (AC) on the microstructural and mechanical properties of Ni-based superalloy coatings, specifically Inconel 718 (IN718), which were applied using high-velocity air fuel (HVAF) methods. Quantitative analyses revealed a 40