Phase High-entropy Alloys Research Articles

Harnessing machine learning for predictive modelling of high entropy alloy phases

Harnessing machine learning for predictive modelling of high entropy alloy phases

Influence of Al and Ti Alloying and Annealing on the Microstructure and Compressive Properties of Cr-Fe-Ni Multi-Principal Element Alloy

This study meticulously examines the influence of aluminum (Al) and titanium (Ti) on the genesis of self-generated ordered phases in high-entropy alloys (HEAs), a class of materials that has garnered considerable attention due to their exceptional multifunctionality and versatile compositional palette. By meticulously tuning the concentrations of Al and Ti, this research delves into the modulation of the in situ self-generated ordered phases’ quantity and distribution within the alloy matrix. The annealing heat treatment outcomes revealed that the strategic incorporation of Al and Ti elements facilitates a phase transformation in the Cr-Fe-Ni medium-entropy alloy, transitioning from a BCC (body-centered cubic) phase to a BCC + FCC (face-centered cubic) phase. Concurrently, this manipulation precipitates the emergence of novel phases, including B2, L21, and σ. This orchestrated phase evolution enacts a synergistic enhancement in mechanical properties through second-phase strengthening and solid solution strengthening, culminating in a marked improvement in the compressive properties of the HEA.

Rhombohedral phase high-entropy alloy of AlMnCuZnBi as a photo-Fenton catalyst for methyl orange degradation

The formation of solid solutions with five or more elements having nearly equiatomic contributions termed as high entropy alloys (HEA), is recent strategy for the development of new materials. Such alloys have displayed superior properties than the conventional ones. The prepared alloys are generally face- or body-centered cubic structures with a few exceptions for orthorhombic and hexagonal close packed structure. In this, we report the synthesis of HEA of AlMnCuZnBi having rhombohedral crystal structure. The material was prepared by vacuum arc melting route. Both the diffractions (X-ray and electron) confirm its structure. The calculated thermodynamic empirical parameters e.g. ΔSmix, ΔHmix, δ, VEC and Ω even validate the feasibility of this single phase solid solution. The HEA had shown feeble ferromagnetic behaviour. The rhombohedral structured HEA may open up new possibilities for various emerging applications. The proposed HEA acted as a catalyst for photo-Fenton like reaction and could degraded methyl orange dye completely in 170 minutes.

Leveraging machine learning to minimize experimental trials and predict hot deformation behaviour in dual phase high entropy alloys

In recent time, high entropy alloys (HEAs) are widely used due to their wide design space and remarkable properties allowing a vast range of property variations with myriads of possible combinations of constituent elements. This research utilizes machine learning techniques, including Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), Decision Tree (DT), and Support Vector Regressor (SVR), to predict the flow stress behavior of the dual FCC phase CoCrCu1.2FeNi high entropy alloy (HEA) at new temperature and strain rates to reduce the experiments dependency. The RF and KNN models were identified as top performers, with R² values of 0.987 and 0.986, respectively. These models successfully predicted flow stresses, aligning closely with actual measurements. A new flow stress-strain curve was generated at new temperature of 1123K, demonstrating good performance metrics for RF and KNN. with R² values of 0.97 and 0.958 respectively. This approach has the potential to significantly reduce the need for extensive experimental work in determining the mechanical behaviour of HEA, offering substantial savings in time, cost, and energy, and marks a significant advancement in the development of such alloys.

Simultaneous enhancement of strength and ductility of Al matrix composites enabled by submicron-sized high-entropy alloy phases

Our study investigated the reinforcing effect of submicron-sized (100–500 nm) Al0.5CoCrCuFeNi high-entropy alloy particles (HEAps) on the tensile properties of Al matrix composites. The HEAps were refined to the submicron-sized level via a thermal plasma process to maximize reinforcing efficiency by increasing specific surface (i.e., interface) area. The Al/HEA composites were produced by hot rolling of mechanically milled Al/HEA composite powder. The microstructural analysis demonstrated that the HEAps were uniformly dispersed within the Al matrix by forming a sound interface without interdiffusion brittle intermetallic layers or any noticeable voids. Moreover, the addition of 2 vol% HEAps resulted in a simultaneous increase in both yield strength from 355 to 403 MPa and tensile elongation from 1.3 to 3.3%. As a result, the composite experienced a synergistic effect due to the combination of intrinsic strengthening to promote uniform plastic deformation and extrinsic strengthening to mitigate plastic instability and crack propagation.

Effect of hot rolling and annealing on microstructure, crystallographic texture, and mechanical properties of Fe11.5 Co20.6 Ni40.7 Cr12.2 Al7.8 Ti7.2 high entropy alloy

The evolution of microstructure and texture during the thermomechanical processing of a Fe-Co-Ni-Cr-Al-Ti dual phase high entropy alloy consists of L12 (γ′) ordered precipitates in the disordered FCC (γ) matrix and its effect on the mechanical properties was investigated. The as-cast alloy was annealed at 1100 °C after 25 % and 50 % hot rolling reduction. The grain size becomes finer, and a considerable amount of annealing twin boundaries (Ʃ3 boundaries) developed in the hot rolling and annealed (HRA) samples. The α-fiber (ND||<110>) texture and twin-related texture components increase after HRA treatment. The mechanical response at room temperature is evaluated employing hardness and uniaxial tensile tests. A significant reduction in hardness, from 448 ± 6 HV to 381 ± 12 HV, is observed in the 50 % hot rolled annealed sample compared to the as-cast sample. Additionally, the yield strength reduced from 779 to 595 MPa with an increment in the ductility from 12.5 to 17.2 % in the as-cast and HR50A samples, respectively. Finally, it has been demonstrated that the increase in coherent twin boundaries and grain size excluding twin boundaries, led to the observed mechanical behavior.

Effects of rolling at different temperatures and subsequent recovery annealing on the mechanical properties of a metastable carbon containing FeMnCoCr high-entropy alloy

Metallic materials or alloys consisting of single face-cubic centered (FCC) phases typically face difficulties in obtaining high strength without sacrificing ductility. The introduction of different crystalline defects via pre-straining and the activation of multiple deformation mechanisms have been shown to be effective in achieving superior strength–ductility synergy. In this work, we investigated the role of varying rolling conditions and subsequent annealing on the mechanical response in an established twinning- and transformation-induced plasticity dual phase high-entropy alloy (TWIP-TRIP-DP HEA). We found that all of the annealed samples exhibited similar nano-twinned structures. Furthermore, phase transformation from FCC γ to hexagonal close-packed (HCP) ε prevailed along the nano-twins under mechanical loading. With increasing rolling temperature, the phase stability of the matrix revealed a downward trend, resulting in a significant increase in TWIP/TRIP effects along with a prominent change in the deformation microstructure. Our study presented a simple and feasible strategy for manipulating mechanical performance by modulating the microstructural characteristics and associated deformation modes.

Breaking data barriers: advancing phase prediction in high entropy alloys through a new machine learning framework

ABSTRACT High entropy alloys (HEAs) represent a promising ADVANCEMENT in the context of Industry 4.0, embodying the principles of interconnectedness, automation and real-time data. The formation of phases in HEAs is predominantly influenced by the composition of the alloy and its corresponding thermodynamic properties. With an expansive composition space, HEAs exhibit diverse phase formations. While previous research has primarily focused on solid solution features, establishing a direct link between compositions and phase formations is crucial for accelerating the development of novel HEAs. Machine learning algorithms have been extensively employed to predict phases in HEAs; however, their effectiveness relies heavily on the availability of large datasets. Unfortunately, acquiring precise data remains challenging due to the nascent nature of HEA research. In this research article, we propose a novel machine learning algorithm to predict alloy phases, even in scenarios with limited data. The primary objective is establishing a robust relationship between elemental properties and phases, enabling phase predictions based on alloy compositions. Our study achieves an accuracy of 92% using a dataset comprising only 118 samples. The proposed work is compared with state-of-the-art approaches, addressing data scarcity challenges in HEAs and advancing predictive modeling for phase predictions in these alloys.

The effect of cooling rate on the precipitation behavior and fracture toughness of the η phase in LDED CoCrFeNiTi high-entropy alloy

Precipitation strengthening is recognized as an effective method for strengthening high-entropy alloys (HEAs) or medium-entropy alloys (MEAs), especially the recently developed CoCrFeNiTi HEA. The η phase plays a crucial role in strengthening CoCrFeNiTix HEAs, though it remains relatively understudied. In this study, CoCrFeNiTi HEA was fabricated by laser directed energy deposition (LDED). The research investigated the effect of cooling rate on the precipitation behavior and fracture toughness of the η phase in non-equilibrium solidified CoCrFeNiTi HEA. The findings revealed that the precipitation of the η phase in the as-deposited LDED sample was restrained, resulting in a very low volume fraction. However, a high-volume-fraction η phase was achieved through annealing at 900 °C for 6 h, followed by air cooling. This approach not only maintained the desired ∼6.2 % volume fraction of the η phase but also prevented its growth and coarsening (∼403 nm), resulting in an optimal combination of microstructure and mechanical properties. The η phase exhibited the Blackburn type orientation relationship with the FCC phase: <11 2‾ 0>η//<1 1‾ 0>FCC and {0001}η//{111}FCC. The L interface of the η phase demonstrated a coherent relationship with the FCC phase ({0001}η//{111}FCC), while the W interface was incoherent with the FCC phase ({11 2‾ 0}η//{1 1‾ 0}FCC). The unique interface characteristics promoted a higher growth rate at the W interface compared to the L interface, leading to the preferred growth of the η phase. The chemical compositions of both the FCC phase and η phase were accurately predicted by experiments and simulations, elucidating the precipitation kinetics of the η phase. When v ≥ vWC, the nucleation and precipitation of the η phase were inhibited. Conversely, when v ≤ vFC, significant coarsening of the η phase occurred. However, when v ≈ vAC, the AC sample exhibited superior mechanical properties, with a microhardness of ∼860 HV and excellent fracture toughness. These findings provide valuable insights and guidance for further enhancement and optimization of HEAs/MEAs strengthened by the η phase.

A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks

Predicting phase stability in high entropy alloys (HEAs), such as phase fractions as functions of composition and temperature, is essential for understanding alloy properties and screening desirable materials. Traditional methods like CALPHAD are computationally intensive for exploring high-dimensional compositional spaces. To address such a challenge, this study explored and compared the effectiveness of random forests (RF) and deep neural networks (DNN) for accelerating materials discovery by building surrogate models of phase stability prediction. For interpolation scenarios (testing on the same order of system as trained), RF models generally produce smaller errors than DNN models. However, for extrapolation scenarios (training on lower-order systems and testing on higher order systems), DNNs generalize more effectively than traditional ML models. DNN demonstrate the potential to predict topologically relevant phase composition when data were missing, making it a powerful predictive tool in materials discovery frameworks. The study uses a CALPHAD dataset of 480 million data points generated from a custom database, available for further model development and benchmarking. Experiments show that DNN models are data-efficient, achieving similar performance with a fraction of the dataset. This work highlights the potential of DNNs in materials discovery, providing a powerful tool for predicting phase stability in HEAs, particularly within the Cr-Hf-Mo-Nb-Ta-Ti-V-W-Zr composition space.

Study on precipitation in (CoCrFeMnNi)90Al6Ti4 high entropy alloy

(CoCrFeMnNi)90Al6Ti4 high entropy alloy (HEA) was prepared by adding Al and Ti elements into CoCrFeMnNi HEA. The alloy underwent processes including copper mold suction casting, 1473 K homogenization, 30 % cold rolling + 1273 K annealing + 1073 K aging, and water quenching and heating treatment. Microstructure and phase composition of both as-cast and heat-treated (CoCrFeMnNi)90Al6Ti4 HEA were studied using metallographic microscope, scanning electron microscope (SEM), energy dispersive spectrometer (EDS), X-ray diffractometer (XRD), and transmission electron microscope (TEM). The effect of heat treatment processes on the precipitation of second phases in (CoCrFeMnNi)90Al6Ti4 HEA was investigated, and mechanical properties of as-cast and heat-treated (CoCrFeMnNi)90Al6Ti4 HEA were studied via hardness tests. The results show that in as-cast (CoCrFeMnNi)90Al6Ti4 HEA, the second phase precipitated is primarily L21 structured AlTiNi2 phase. After homogenization, cold rolling, annealing, and aging processes, both the quantity and size of the second phase increase, with a small amount of nanoscale L12 phase appearing after aging treatment. With the progression of heat treatment, the hardness of (CoCrFeMnNi)90Al6Ti4 HEA increases, and mechanical properties are improved.

Effects of Er2O3 contribution on microstructure and mechanical properties of high entropy and dual phase alloys

The study aimed to look at the differences between a high entropy alloy and a dual phase high entropy alloy with high iron and Mn content. It also wanted to find out what happened when a small amount of the rare earth element erbium oxide (Er2O3) was added to these alloys, especially on the mechanical and microstructure of the alloy. The first step in this study was to get 99 % pure metal powders, which were then made by dividing the powders into 30 g of equal weight. The powders were mixed evenly and then pressed into 10-g blocks under 6 tons of pressure. They were then set up to be melted in an arc. Four of the eight samples that were melted in an arc under vacuum were treated with heat. The four different compositions were then turned into small metallographic samples for SEM, EDX, Mapping, XRD, and microhardness tests. The study's imaging and analysis processes were also carried out. After that, SEM images were taken at various magnifications (100X, 500X, 1000X, 2500X, and 5000X), and EDS and XRD analyses were done on different structures found in these images. These analyses were based on literature research about the clarity of the metallographic images in the alloy, which showed the study's metallographic differences. The samples were put through microhardness and wear tests to see which one was stronger and less worn. The dual phase high entropy alloy (A2) was stronger and less worn than the high entropy alloy (A1). Er2O3 had little effect on the alloys' mechanical properties or microstructure. The heat treatment that was done to the samples helped the metallographic structure. In terms of physics, it didn't change the hardness much, but it did make the material wear less quickly.

Tailoring high-entropy alloys via commodity powders for metal injection moulding: A feasibility study

High entropy alloys (HEAs) represent a novel frontier in metallurgical advancements, offering exceptional mechanical properties owing to their unique multicomponent nature. This study explores a novel strategy utilising commodity powders - Ni625, Invar36, and CoCrF75 - to tailor HEAs via metal injection moulding (MIM). The objective is to achieve cost-effective manufacturing while maintaining desired properties. The research involves mixing these powders in a specific proportion, integrating them with a sustainable polyethylene glycol-cellulose acetate butyrate binder system, and characterising the resulting feedstocks for MIM processing. Subsequent debinding and sintering steps were executed to densify and form a single face-centered cubic (FCC) phase HEA, followed by comprehensive analyses to evaluate the suitability of the developed HEA compositions. In addition, all MIM stages were thoroughly characterised to control the porosity of the final parts and to ensure a single FCC solid solution with promising mechanical properties in the developed non-equiatomic CoCrFeNiMox-type HEAs.

Advancements in machine learning for predicting phases in high-entropy alloys: a comprehensive review

High entropy alloys (HEAs) are distinguished by their enhanced physicochemical properties, attributed to the formation of various phases such as solid solution (SS), intermetallic (IM), or a combination (SS + IM). These phases contribute distinctively to the microstructure of the alloys. A critical aspect of alloy design revolves around accurately predicting these phases, which has led to the integration of sophisticated data vetting methods and Machine Learning (ML) algorithms in recent research. This review paper aims to provide a comprehensive analysis of the advancements in phase prediction accuracy within HEAs, an essential component in the development of these alloys. HEAs are known for their intricate compositions, offering a wide spectrum of material properties, making them particularly relevant for applications aimed at future sustainability. Phase engineering in HEAs unlocks the potential for creating materials tailored to eco-friendly technologies and energy-efficient solutions. The challenge in predicting phase selection in HEAs is accentuated by the limited data available on these complex materials. This review delves into how advanced data vetting techniques and ML algorithms are being employed to overcome these challenges, thus contributing significantly to sustainable material design. The paper examines various algorithms used in HEA phase prediction, including KNN (K-Nearest Neighbors), SVM (Support Vector Machines), ANN (Artificial Neural Networks), GNB (Gaussian Naive Bayes), and RF (Random Forest). It discusses the testing accuracy of these algorithms in classifying HEA phases, revealing variations in their effectiveness. The review highlights the superior accuracy of ANNs, followed closely by KNN and SVM, while noting the comparatively lower accuracy of GNB. This comprehensive review synthesizes current research efforts in utilizing computational methods to design HEAs, underlining their broader implications in expediting the discovery and development of diverse metal alloys. These efforts are pivotal in meeting the evolving demands of modern engineering applications, thereby contributing to the advancement of materials science.

Structural-regulation of Laves phase high-entropy alloys to catalytically enhance hydrogen desorption from MgH2

Magnesium hydride (MgH2) is a promising hydrogen storage material, but slow hydrogen desorption kinetics and poor cycling stability still limit its development. Catalytic doping was demonstrated to provide effective solution for it. In this work, we develop novel Zr-based high-entropy alloys (HEAs) catalysts with different Laves phase structures to improve the hydrogen storage in MgH2. It was demonstrated that the catalyst grain size, defect density and lattice distortion have a great influence on the performance of HEAs catalysts. Specially, the C15 type Laves phase HEA exhibits a better catalytic effect than that of C14 type. The introduction of HEAs with "hydrogen pump" effect would shift the rate-limiting step of MgH2 hydrogen desorption from two-dimensional nucleation growth to two-dimension phase boundary migration, which makes it exhibit both great better hydrogen desorption kinetics enhanced by nine times and cycling stability with 92 % capacity retention after 30 cycles. This work not only highlights the impact of Laves phase crystal structure regulation on HEAs catalysts, but also provides insights into the design and development of HEAs catalysts to improve the hydrogen storage performance of MgH2.

First-principles calculation of phase transitions and mechanical properties of (CoCrNi)100−xAlx (0 ≤ x ≤ 28 at. %) high-entropy alloys

In the two-phase high-entropy alloys (HEAs) (i.e., FCC, BCC), the modulation of the BCC phase is crucial for improving the mechanical properties of FCC-type HEAs. The stability of the phase of (CoCrNi)100−xAlx (0 ≤ x ≤ 28 at. %) HEAs is studied using first-principles calculations. The Al content on the phase transition of CoCrNi HEAs is discussed. The theoretical values of lattice parameter a (x) increase with increasing Al concentration, which is consistent with the earlier experimental findings. The crystal structure transitions from the FCC to BCC crystal structure as the Al content increases. At x &amp;lt; 11.8 at. %, Al alloying lowers the elastic stability of the BCC and FCC phases, whereas excessive Al doping causes the FCC phase to BCC phase transition (x &amp;gt; 21.4 at. %). The crystal structure has an ideal mix phase of BCC and FCC at x = 18.8 at. %, which results in excellent strength-ductility synergy of HEAs. There is a phase transition point at x = 11.8 at. %, where there may be a competition between phase transition and dislocation nucleation, which improves strength. The work in this paper provides new ideas for the design of future high-performance duplex phase HEAs.

Machine learning in high-entropy alloys: phase formation predictions with artificial neural networks

Due to their complex compositions, high entropy alloys (HEAs) offer a diverse range of material properties, making them highly adaptable for various applications, including those crucial for future sustainability. Phase engineering in HEAs presents a unique opportunity to tailor materials for environmentally friendly technologies and energy-efficient solutions. However, the challenge of predicting phase selection, a key aspect in harnessing the full potential of HEAs for sustainable applications, is compounded by the limited availability of HEA data. This study presents a distinctive approach by using a precisely produced and selected dataset to train an artificial neural network (ANN) model. This dataset, unlike prior studies, is uniquely constructed to contain an equal amount of training data for each phase in HEAs, which includes single-phase solid solutions (SS), amorphous (AM), and intermetallic compounds (IM). This methodology is relatively unexplored in the field and addresses the imbalanced data issue common in HEA research. To accurately assess the model's performance, rigorous cross-validation was employed to systematically adapt the model's hyperparameters for phase formation prediction. The assessment includes metrics such as phase-wise accuracy (AM 86.67% SS 81.25% &amp; IM 82.35%), confusion matrix, and Micro-F1 score (0.83), all of which collectively demonstrate the effectiveness of this approach. The study highlights the importance of feature parameters in phase prediction for HEAs, shedding light on the factors influencing phase selection. Its balanced dataset and training method notably advance machine learning in HEA phase prediction, providing valuable insights for material design amidst challenges and data scarcity in the field.

Composition of Intermetallic Phases in High-Entropy Alloys: Compositional Inheritance of High-Entropy Sigma Phases from Binary Counterparts

Composition of Intermetallic Phases in High-Entropy Alloys: Compositional Inheritance of High-Entropy Sigma Phases from Binary Counterparts

Synthesis and characterization of FeAlZnCrCuMg alloy using high energy ball milling

A High entropy alloy of more than four equi-atomic components has been produced by a high energy ball mill. Metallic powders of each component in equi-atomic ratio were milled in wet conditions. The X-ray diffraction (XRD) pattern for the samples, which were collected at regular intervals of time, displayed the process of formation of single phase high entropy alloy. Body centered cubic structure of the FeAlZnCrCuMg alloy was confirmed after 25 h of milling. The lattice parameter of the alloys has been found to be 2.88 Å. Transmission electron microscopy (TEM) analysis of the sample also confirmed the formation of single phase high entropy alloy.

Novel refractory high-entropy metal-ceramic composites with superior mechanical properties

A new concept of refractory high-entropy metal-ceramic composites (HEMCC) has been proposed that combines the outstanding physical properties of both high-entropy alloy (HEA) and high-entropy ceramic (HEC). As the first HEMCC system, to the best of our knowledge, TiTaNbZr-(TiTaNbZr)C, has been developed by a powder metallurgy process. The HEA and HEC phases exhibit body-centered cubic (BCC) and rock-salt B1 crystal structures, respectively, and both phases have non-equimolar chemical compositions. With the increase of the HEC phase in HEMCC, the hardness is enhanced while the density and fracture toughness are decreased. HEA50C sintered from 50 vol% HEA and 50 vol% HEC precursor powders shows a favorable combination of flexural strength (541 ± 48 MPa) and fracture toughness (6.93 ± 0.27 MPa·m1/2) at room temperature and a high compressive strength at 1300 °C (275 MPa). The optimized mechanical performance of HEMCC might be attributed to the combination of the ductile HEA and strong HEC phases, smaller grain size, and crack arrest at HEC/HEA interfaces.