Alloy System Research Articles

Database of Ternary Amorphous Alloys Based on Machine Learning

Abstract The unique long-range disordered atomic arrangement inherent in amorphous materials endows them with a range of superior properties, rendering them highly promising for applications in catalysis, medicine, and battery technology, among other fields. Since not all materials can be synthesized into an amorphous structure, the composition design of amorphous materials holds significant importance. Machine learning offers a valuable alternative to traditional “trial-and-error” methods by predicting properties through experimental data, thus providing efficient guidance in material design. In this study, we develop a machine learning workflow to predict the critical casting diameter, glass transition temperature, and Young’s modulus for 45 ternary reported amorphous alloy systems. The predicted results have been organized into a database, enabling direct retrieval of predicted values based on compositional information. Furthermore, the applications of high glass forming ability region screening for specified system, multi-property target system screening and high glass forming ability region search through iteration are also demonstrated. By utilizing machine learning predictions, researchers can effectively narrow the experimental scope and expedite the exploration of compositions.

Investigation of phase transformation and mechanical properties of silicon addition on AlCrFeMnNi high entropy alloys

Abstract This paper examines the impact of silicon in the AlCrFeMnNi high-entropy alloy system, focusing on both its microstructural and mechanical properties. Alloys with varying silicon content (x = 0, 0.3, 0.6, 0.9 atomic ratio) were synthesized using vacuum arc melting. The phase formation of these high-entropy alloys was analyzed using X-ray diffraction to comprehend the alloying process behaviour. The findings revealed that the solidification of the AlCrFeMnNi alloy occurred in dendritically, with dendrite cores containing Cr, Fe, and Ni, while interdendritic regions were enriched in Al and Ni after adding Silicon. Increasing the silicon content from 0 to 0.9 led to significant improvements in microhardness and wear resistance. This improvement is attributed to the reinforcement of grain boundaries provided by silicon. The formation of an Al and Ni rich B2 phase is crucial in resisting dislocation motion and preventing further deformation. Additionally, the addition of silicon led to improved corrosion resistance, as demonstrated by potentiodynamic polarization measurements. However, a trade-off was observed between compressive strength and ductility: compressive strength increased with higher silicon concentrations, but at the expense of ductility.

Micro-friction Mechanism Characterization of Particle-Reinforced Multilayer Systems of 316L and 430L Alloys on Gray Cast Iron

Micro-friction Mechanism Characterization of Particle-Reinforced Multilayer Systems of 316L and 430L Alloys on Gray Cast Iron

The Role of Al/Ti in Precipitate-Strengthened and Austenite-Toughened Co-Free Maraging Stainless Steel

The strength of ultra-low carbon maraging stainless steels can be significantly enhanced by precipitating nanoscale intermetallic secondary phases. Retained or reversed austenite in the steel can improve its toughness, which is key to achieving an ideal combination of strength and toughness. Ti and Al are often used as cost-effective strengthening elements in maraging stainless steels but the synergistic toughening and strengthening mechanisms of Ti and Al have not been studied. To investigate the synergistic toughening and strengthening mechanisms of Ti and Al in Co-free maraging stainless steels, this paper focuses on the microstructure and mechanical properties of three alloys: Fe-12Cr-11Ni-1.7Al-0.5Ti (Steel A), Fe-12Cr-11Ni-0.5Ti (Steel B), and Fe-12Cr-11Ni-1.7Al (Steel C). The impact of Ti and Al on the microstructure and mechanical properties was investigated using X-ray diffraction (XRD), high-resolution transmission electron microscopy (TEM), and thermodynamic simulations. The relationship between microstructure, strength, and toughness is also discussed. The results indicated that Steel A, containing both Al and Ti, exhibited the highest strength level after solution treatment at 900 °C, with an ultimate tensile strength reaching 1571 MPa after aging at 540 °C. This is attributed to the simultaneous precipitation of spherical β-NiAl and rod-shaped η-Ni3Ti phases. Steel B, with only Ti, formed a significant amount of Ni-rich reversed austenite during aging, reducing its ultimate tensile strength to 1096 MPa. Steel C, with only Al, showed a high strength–toughness combination, which was achieved by forming dispersive nano-sized intermetallic precipitates of β-NiAl in the martensitic matrix with a slight amount of austenite. It is highlighted that Al has superior toughening and strengthening effects compared to Ti in the alloy system.

An Analytical Study for Explosive Grain Initiation

The most common form of solidification of metals is heterogeneous nucleation, in which the particles, regardless of whether they are endogenous or exogenous, nucleate the primary crystal phase, becoming solid crystal particles and, subsequently, initiating into grains during solidification. Explosive grain initiation has been proposed recently for these particles, which have significant nucleation undercooling, in which once nucleation happens, a certain number of solid particles can initiate into grains simultaneously, resulting in recalescence. This is a different form of grain initiation and has high potential for more significant grain refinement in casting alloys. In this work, an analytical model is designed to describe explosive grain initiation, based on which the criteria for the three different grain initiation forms, explosive grain initiation (EGI), hybrid grain initiation (HGI), and progressive grain initiation (PGI), are derived. These criteria are employed to develop a grain initiation map for the Mg-Al alloy system inoculated with nucleant particles having a log-normal size distribution. This work can not only help us to understand the effect of each condition, such as the cooling rate and the solute concentration, on grain initiation behaviors, but also predict the grain size for alloy systems with relatively impotent nucleant particles during solidification.

Microstructural Insights and Composition Analysis of a Fractured Brass Alloy of Unknown Composition Using Advanced Characterization Techniques

This study aims to investigate the microstructural and compositional characteristics of a fractured brass alloy using energy-dispersive X-ray spectroscopy (EDS), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The brass alloy, initially of unknown composition, was determined to consist of 63.65 at% Cu, and 36.35 at% Zn through EDS analysis. Microstructural analysis via SEM revealed a typical ductile fracture pattern with necking and dimple formation, indicative of significant plastic deformation prior to failure. The alloy exhibited a grain size ranging from 10 µm to 80 µm and displayed both α-phase (FCC structure) and possible traces of β-phase, though XRD detected only the α-phase due to the low concentration of β-phase (<5%). A comparison between EDS and XRD results for analyzing chemical composition showed that good assumptions could be made from the lattice parameters of the binary alloy following Nelson-Relay function to predict an approximate composition in binary alloy systems, which was found to be 34 at% Zn in our analysis. Furthermore, the sample’s crystal structure was confirmed as FCC, which supported its observed ductility and plastic deformation prior to fracture. The SEM analysis also confirmed that the material went through an annealing process. The fracture morphology further showed that the FCC structure and the annealing process both contributed to the material’s increased ductility. The analysis further confirms that EDS is more reliable for chemical composition determination in an alloy system. Evidence of annealing twinning in the SEM images suggests the sample underwent an annealing process, contributing to its enhanced ductility.

Additive manufacturing of heat-resistant aluminum alloys: a review

Abstract The capability for synergistic advancements in both making and shaping afforded by additive manufacturing (AM) enables the flexible production of high-performance components. Boosted by the growing demand for heat-resistant aluminum alloys in the moderate-temperature weight-critical applications, AM of heat-resistant aluminum alloys constitutes a burgeoning field. Although numerous advances have emerged in recent years, there remains a gap in the review literature elucidating the newly-developed alloy systems and critically evaluating the efficacy. This state-of-the-art review presents a detailed overview of recent achievements on the heat-resistant aluminum alloy development. It begins with the introduction of various AM technologies and the pros and cons of each technique are evaluated. The enhancement mechanisms associated with printability and high-temperature properties of AM aluminum alloys are then delineated. Thereafter, the various additively manufactured aluminum alloy systems are discussed with regard to the microstructure, heat resistance and high-temperature performance. An emphasis is put on the powder bed fusion-laser beam (PBF-LB) as it has garnered significant attention for heat-resistant aluminum alloys and the vast majority of the current studies are based on this technique. Finally, perspectives are outlined to provide guidance for future research.

Quenching high-temperature phase in Cu–Sn alloy system by femtosecond and picosecond laser irradiation

Quenching high-temperature phase in Cu–Sn alloy system by femtosecond and picosecond laser irradiation

Experimental verification of a common assumption in the use of concentration-dependent interdiffusion coefficient

There has generally been a view that concentration-dependent interdiffusion coefficient, D(C), obtained at any diffusion temperature and time is a material constant and can be reliably used to predict diffusion effects at all other isothermal diffusion times. This notion which is implicitly predicated on the assumption that diffusion-induced stress (DIS) rapidly and completely relaxes once formed and does not influence D(C) is experimentally verified in the present work. The results of the study show that reliably computed D(C) from concentration profile obtained at long diffusion time fails to correctly predict concentration profiles at shorter diffusion periods, due to isothermal variation of the D(C) with time. Accordingly, instead of continuing to assume that D(C) is time-independent and can be reliably used to predict diffusion effects at any isothermal diffusion time, without appropriate experimental support, proper experimental verification of this crucial concept in other alloy systems, as done in the present work, is imperative.

Deformation behavior of Gd alloyed CoCrFeNi high entropy alloy at high temperature under the influence of local nano-ordered structure and misorientation

The thermomechanical processing (TMP), which is an industrial process that includes deformation and heat treatment, represents a novel approach to enhancing the mechanical properties of metallic materials. However, it requires extensive research on the high-temperature deformation behavior of alloys during its application process. The present study conducts hot compression tests at 700 °C and 800 °C, as well as detailed microstructural characterization of the deformed material after hot deformation, to comprehensively discuss the mechanical properties and deformation behavior at high temperature of the (CoCrFeNi)100-xGdx (x = 0, 0.5, 1, 2, 3 at.%) HEAs. The (CoCrFeNi)100-xGdx HEAs exhibit an excellent combination of strength and plasticity in high temperature. The original dendritic microstructure in the (CoCrFeNi)100-xGdx HEAs was completely fractured and transformed into a mass of deformed structures during thermal deformation. The mechanical properties at high temperature of the alloy system originate from the misorientation in the microstructure. Dislocation slip is the main deformation mechanism of the alloy system at high temperature. The coordinated deformation of the soft orientation for FCC phase and the hard orientation for HS phase affects the dislocation morphology in the microstructure. Dynamic recovery (DRV) plays a dominant role in the flow behavior of the alloy at high temperature, and work hardening is the main strengthening mechanism during plastic deformation. The correlation between microstructure evolution and mechanical properties of Gd alloyed CoCrFeNi HEAs during hot deformation has been established through comprehensive studies on deformation behavior.

Review of Laser Powder Bed Fusion’s Microstructure and Mechanical Characteristics for Al-Ce Alloys

As a new alloy manufacturing method that can break through the limitations of molds to manufacture fine parts, laser powder bed fusion has recently become a common process for producing aluminum alloys. In the fields of aerospace or automotive, aluminum alloys with both good printability and good mechanical performance in high-temperature conditions are greatly demanded, and the Al-Ce alloy is one of the alloys with significant potential. Therefore, systematic research on the additive manufacturing of Al-Ce alloys is still being explored. Herein, we review the recent progress and current status of laser powder bed fusion-produced Al-Ce alloys, giving our opinions on the development of this alloy system. Element composition, alloy powders, laser powder bed fusion parameters, microstructures, and mechanical properties at room temperature and high temperatures are summarized. The choice of alloying strategies is crucial for a specific mechanical improvement of the Al-Ce alloys. Finally, the details of the Al-Ce alloys manufactured via laser powder bed fusion are presented.

Effect of metalloid element on the microstructural and mechanical properties of AlCoCrCuFeNi high-entropy alloys

ABSTRACT The impact of the metalloid element silicon (Si) addition on the microstructural and mechanical properties of the AlCoCuCrFeNiSix high-entropy alloy system is examined in this paper. The alloys were synthesized using a vacuum arc melting route. X-ray diffraction was used to analyse the current high-entropy alloys’ phase formation to comprehend the alloying process’s behaviour. It is evident from the peak pattern of the X-ray diffraction that the inclusion of Si promotes the growth of body-centred cubic structures. The microhardness and wear resistance were increased by increasing the Si content from 0 to 0.9. Si presence enhances the hardness of the alloys and strengthens the grain boundary. Improved hardness and wear resistance results from the enhanced body-centred cubic-phase formation, which poses a barrier to the dislocation movement and prevents further deformation. Furthermore, the inclusion of Si improved corrosion resistance in potentiodynamic polarization measurements. Excellent compressive strength is possessed by all of the high-entropy alloys with Si addition.

Investigation of self-diffusion and specific heat capacity of eutectic al-si systems using molecular dynamics simulations with ADP

Aluminum-silicon (Al-Si) alloys are of significant interest in various engineering applications due to their favorable mechanical properties and low density. Understanding the thermodynamic and transport properties of these alloys is essential for optimizing their performance in practical applications. In this study, an angular dependent potential (ADP) for Al-Si systems is employed to describe the atomic interactions and dynamics of an Al-Si alloy system. A set of molecular dynamics (MD) simulations are performed to explore the diffusion behavior of the Al-Si alloy at different temperatures, with a specific focus on the eutectic composition of the Al-Si system. The mean squared displacement (MSD) method is applied to calculate its diffusion coefficients, enabling a detailed analysis of the system’s atomic mobility and transport characteristics. Furthermore, the specific heat capacity of the Al-Si system is determined through energy fluctuation analysis, providing insights into the system’s thermodynamic behavior. The obtained diffusion coefficients play a vital role in predicting the kinetics of the eutectic solidification of Al-Si alloys, while the specific heat capacity will facilitate the understanding of the thermal behavior and the energy changes associated with the eutectic reaction in such alloys.

An alloy-agnostic machine learning framework for process mapping in laser powder bed fusion

PurposeThis study aims to introduce an image-based method to determine the processing window for a given alloy system using laser powder bed fusion equipment based on achieving the desired melting mode across multiple materials for powder-free specimens. The method uses a convolutional neural network trained to classify different track morphologies across different alloy systems to select appropriate printing settings. This method is intended for the development of new alloy systems, where the powder feedstock may be unavailable, or prohibitively expensive to manufacture.Design/methodology/approachA convolutional neural network is designed from scratch to identify the 4 key melting modes that are observed in laser powder bed fusion additive manufacturing across different alloy systems. To increase the prediction accuracy and generalisation accuracy across different materials, the network is trained using a novel hybrid data set that combines fully unsupervised learning with semi-supervised learning.FindingsThis study demonstrates that our convolutional network with a novel hybrid training approach can be generalised across different materials, and k-fold validation shows that the model retains good accuracy with changing training conditions. The model can predict the processing maps for the different alloys with an accuracy of up to 96% in some cases. It is also shown that powder-free single-track experiments are a useful indicator for predicting the final print quality of a component.Originality/valueThe “invariant information clustering” (IIC) approach is applied to process optimisation for additive manufacturing, and a novel hybrid data set construction approach that accounts for uncertainty in the ground truth data, enables the trained convolutional model to perform across a range of different materials and most importantly, generalise to materials outside of the training data set. Compared to the traditional cross-sectioning approach, this method considers the whole length of the single track when determining the melting mode.

Study on the mechanism of improvement of mechanical properties of Al–Mg–Si aluminum alloys by Cu and Ce

First, the stability and tensile properties of the Al/Mg2Si interface doped with alloying elements in the Al–Mg–Si system of aluminum alloys are calculated from first principles. Then, the two experimental alloys with different components were heat treated and the properties were characterized. Calculations show that when the interface is doped with Cu, the tensile stress that the interface can withstand increases to 3.13 GPa; when the interface is doped with Cu and Ce, the tensile strain that the interface can withstand increases to 20% and the tensile stress that the interface can withstand increases to 4.17 GPa; when the interface is doped with Cu and Sc, the tensile strain that the interface can withstand increases to more than 25% and the tensile stress that the interface can withstand increases to 5.08 GPa or more. Characterization of the organization and properties of the experimental alloys showed that Ce not only increased the peak solid solution temperature of the alloys, but also increased the tensile and yield strengths of the alloys in the extruded state by 16% and 13.74%, respectively. During hot extrusion, Ce was able to promote the precipitation of precipitated phases. During aging, the addition of Ce promoted the aging precipitation of the alloy, which not only increased the number of precipitated phases in the alloy, but also refined the precipitated phases and enhanced the aging strengthening effect of the alloy. This study provides technical guidance for the development of new aluminum alloy materials with high strength and low cost.

Enhancing magnetostriction in FeAl alloys via crystal growth orientation tuning by a trace amount of Pt doping

Iron-based cubic alloy systems opened the door to the development of magnetostrictive materials with moderate magnetostriction, low cost, and good mechanical properties. At the as-cast state, the grain growth orientation of these alloy systems is close to random, which deteriorates magnetostriction. Doping with certain elements can tune the growth orientation to enhance magnetostriction. However, the driving force for the crystal growth along preferential orientation is still not clear. Here in this work, revealed by density functional theory (DFT) calculation, Pt doping can tune the crystal growth direction of FeAl along <100> crystal direction (same as easy magnetization direction), which can be utilized to realize 90° domain-switching and thus magnetostriction is enhanced greatly. These are confirmed by experimental results. The magnetostriction increases from 29 ppm to 80 ppm, representing a 176% enhancement through doping with 0.4 at.% Pt. The maze magnetic domain structures, observed by magnetic force microscope, are also preferentially <100> oriented and feature stripes. In consideration of the similarity between this study and our previous study on FeGa alloy (C. Zhou et al., 2020), it can be expected that our methods can be extended to other magnetostrictive materials and help develop polycrystals with desired orientations.

A novel pretreatment approach on titanium alloys for electroless nickel

To augment the wear resistance of the titanium alloy, electroless nickel (EN) is an effective method to strengthen the surface properties. However, the oxide layer on the surface of titanium alloy will inhibit the adhesion of EN. Meanwhile, the traditional pretreatment methods are cumbersome steps and time-consuming. To overcome these limitations, this study systematically explores a one-step pretreatment approach through amino tris(methylenephosphonic acid) (ATMPA) and HF, which is designed to improve the binding strength of EN. The results exhibit that the compact composite membrane with Ti-ATMPA and TiF3 are successfully prepared under pH 3.0, which has provided a pretreatment layer to replace the oxidation of titanium alloy during EN process, and the adhesion of EN coating (EN-3.0) based on the pretreatment layer reaches 22.46 MPa. The wear resistance of titanium alloy and EN plating system studied via a friction measurement, and the lowest wear rate exhibit in EN-3.0 plating (880.9 × 10-10mm3/Nm) that is lower than EN-traditional pretreatment with a decrease of 32 %. Moreover, the formation and catalytic model of medial layer are also discussed.

The Dispersion-Strengthening Effect of TiN Nanoparticles Evoked by Ex Situ Nitridation of Gas-Atomized, NiCu-Based Alloy 400 in Fluidized Bed Reactor for Laser Powder Bed Fusion

Throughout recent years, the implementation of nanoparticles into the microstructure of additively manufactured (AM) parts has gained great attention in the material science community. The dispersion strengthening (DS) effect achieved leads to a substantial improvement in the mechanical properties of the alloy used. In this work, an ex situ approach of powder conditioning prior to the AM process as per a newly developed fluidized bed reactor (FBR) was applied to a titanium-enriched variant of the NiCu-based Alloy 400. Powders were investigated before and after FBR exposure, and it was found that the conditioning led to a significant increase in the TiN formation along grain boundaries. Manufactured to parts via laser-based powder bed fusion of metals (PBF-LB/M), the ex situ FBR approach not only revealed a superior microstructure compared to unconditioned parts but also with respect to a recently introduced in situ approach based on a gas atomization reaction synthesis (GARS). A substantially higher number of nanoparticles formed along cell walls and enabled an effective suppression of dislocation movement, resulting in excellent tensile, creep, and fatigue properties, even at elevated temperatures up to 750 °C. Such outstanding properties have never been documented for AM-processed Alloy 400, which is why the demonstrated FBR ex situ conditioning marks a promising modification route for future alloy systems.

Construction of Al–Si interatomic potential based on Bayesian active learning

Nanoscale simulations for optimizing the performance and processing of Al–Si alloys are currently facing two major obstacles: the scarcity of high-quality semi-empirical potentials tailored to complex alloy systems, and the prohibitively high computational cost associated with ab initio molecular dynamics simulations. In order to enhance simulation efficiency and accuracy of the Al–Si alloys’ microstructural evolution, this study employs a dynamic active learning technique, FLARE, to develop a non-parametric machine learning potential that combines the high accuracy of density functional theory (DFT) with the efficiency of classical molecular dynamics (MD). Without relying on extensive initial ab initio molecular dynamics data or existing databases, collection of the necessary data is progressively made during the active learning process, thereby constructing a potential capable of accurately simulating the structure and dynamics of high-temperature Al–Si alloys. By comparing with experimental measurements and ab initio molecular dynamics calculations, the high accuracy and computational efficiency of this potential is demonstrated in predicting energy, force, structure, and dynamic properties. The results provide novel theoretical insights for optimizing Al–Si alloy processing and underscore the usefulness of active learning methods in constructing high-accuracy potentials.

Microstructure and properties of Cu-3Ti-0.3Cr-0.15Mg alloy designed via machine learning

With the development of information technology, machine learning (ML) has been widely applied in materials science. ML can avoid a multitude of trial and error experiments and improve the efficiency of discovering new materials. Hence, screening the alloys with excellent properties by machine learning is time-saving and effective. In this work, the major element descriptors were screened by importance analysis and correlation analysis as input features, and common ML algorithms were considered to establish ML model. The final ‘composition-process-property’ alloy design strategy was proposed and applied to guide the selection of alloy systems and composition. As a result, the Cu-3Ti-0.3Cr-0.15 Mg alloy was screened based on the extreme gradient boosting (XGBoost) model. The ultimate tensile strength and electrical conductivity of 1018 MPa and 20.1 % IACS were achieved for the designed alloy, respectively, which is well consistent with the predicted values. The ultra-high strength for the designed alloy can be mainly attributed to the precipitation strengthening. Specifically, the formation of nanoscale β′-Cu4Ti and Cr precipitates during the heat treatment plays a significant role in enhancing the strength of the alloy. This work offers new insights into the design of Cu-Ti alloys with excellent comprehensive properties by employing a machine learning model trained on small data sets, which also provides a potential to be applied to other materials systems.