Multicomponent Aluminum Alloys Research Articles

Modelling the spatial evolution of excess vacancies and its influence on age hardening behaviors in multicomponent aluminium alloys

Excess vacancies play crucial roles in the precipitation of age-hardening precipitates in aluminium alloys, but their spatial evolution across grains during heat treatments is less known. In this work, a numerical model is developed to predict the spatial evolution of non-equilibrium excess vacancies during cooling from solution treatment and during ageing heat treatments of multicomponent aluminium alloys. A finite volume scheme is applied to derive the spatial distribution of vacancy site fraction across grains by solving the diffusion equations of vacancies under the influence of solute elements. Binding energies between solute atoms and vacancies predicted by first-principles calculations has been used to handle the trapping of excess vacancies by solute atoms and atom clusters. The annihilation rates of excess vacancies at grain boundaries and at dislocation jogs have been derived based on a rigorous description of the annihilation mechanisms of vacancies. The evolution of the density of dislocation jogs due to vacancy annihilation has been taken into account. The model is successfully applied to interpret the age hardening behaviors of experimental alloys subjected to different thermomechanical processing conditions. This model will help to reach a deeper understanding of the roles of excess vacancies in precipitation kinetics and therefore is important to further optimize thermomechanical processing parameters and alloy composition to improve the macroscopic mechanical properties of age hardening aluminium alloys.

Modelling of solidification structure and macro-segregation in an industrial DC cast slab

A cellular automaton (CA)-finite element (FE) model was implemented for multi-scale modelling of micro-segregation, mesoscopic grain structure and macroscopic segregation during direct chill (DC) casting of industrial billets or slabs. The macroscopic transport of mass, momentum, energy and solutes was solved on an FE grid, while the mesoscopic grain structure governed by nucleation, growth kinetics and grain evolution was calculated on a CA grid. The solidification path was determined using a modified micro-segregation model for multi-component aluminium alloys. An Euler representation was used for pre-processing and post-processing, and a Lagrangian representation was used for expanding the calculation domain and for resolving the CAFE model. By simulating a DC casting experiment of the 2024 aluminium alloy, a typical grain structure was reproduced, and the composition map showed a reasonable deviation. This model was applied to an industrial-scale DC cast slab of an Al-3.5Cu-1.5 Mg (wt. %) alloy, and three simulations with different nucleation undercoolings were performed for a grain-unrefined slab, a grain-refined slab and an equilibrium solidified slab, respectively. The slabs tended to solidify at equilibrium with the decreasing nucleation undercooling. The earlier release of latent heat yielded a smaller liquid undercooling region ahead of the solidification front, and a finer grain structure. A typical grain structure with coarse equiaxial grains at the centre and fine columnar grains near the bottom surface as well as sidewall was observed for the grain-unrefined slab. By contrast, the grain structure of the grain-refined slab was fully equiaxial. Furthermore, the grain structure, temperature field, melt flow and macro-segregation were quantitatively investigated.

Towards high-throughput microstructure simulation in compositionally complex alloys via machine learning

The coupling of computational thermodynamics and kinetics has been the central research theme in Integrated Computational Material Engineering (ICME). Two major bottlenecks in implementing this coupling and performing efficient ICME-guided high-throughput multi-component industrial alloys discovery or process parameters optimization, are slow responses in kinetic calculations to a given set of compositions and processing conditions and the quality of a large amount of calculated thermodynamic data. Here, we employ machine learning techniques to eliminate them, including (1) intelligent corrupt data detection and re-interpolation (i.e. data purge/cleaning) to a big tabulated thermodynamic dataset based on an unsupervised learning algorithm and (2) parameterization via artificial neural networks of the purged big thermodynamic dataset into a non-linear equation consisting of base functions and parameterization coefficients. The two techniques enable the efficient linkage of high-quality data with a previously developed microstructure model. This proposed approach not only improves the model performance by eliminating the interference of the corrupt data and stability due to the boundedness and continuity of the obtained non-linear equation but also dramatically reduces the running time and demand for computer physical memory simultaneously. The high computational robustness, efficiency, and accuracy, which are prerequisites for high-throughput computing, are verified by a series of case studies on multi-component aluminum, steel, and high-entropy alloys. The proposed data purge and parameterization methods are expected to apply to various microstructure simulation approaches or to bridging the multi-scale simulation where handling a large amount of input data is required. It is concluded that machine learning is a valuable tool in fueling the development of ICME and high throughput materials simulations.

Rationalizing Al-Mg, Al-Mg-Si, and multicomponent aluminum alloy design with segregation susceptibility predictions

Macro-segregation is a type of unacceptable defects, which normally occurs for aluminum alloys and other nonferrous alloys. In this work, the macro-segregation susceptibility (MAS) of commercial 5xxx and 6xxx alloys and Al-Mg and Al-Mg-Si alloys have been investigated via a criterion based on the solidification curves from thermodynamic calculations. Equilibrium solute distribution coefficients, k, with different initial alloying concentrations and their relationships with the MAS are discussed. Of all commercial 5xxx and 6xxx aluminum alloys, the AA5754, AA5182, AA6181A and AA6016 alloys have shown less susceptible to macro-segregation than others. Of all Al-Mg and Al-Mg-Si alloys in this study, Al-Mg alloys with higher Mg, and Al-Mg-Si alloys with both higher Mg and Si have shown less susceptible to macro-segregation than other compositions. Finally, the role of mass transfer behavior in improving macro-segregation is discussed from the perspective of thermodynamic and kinetic calculations.

Thermodynamic Calculation of the Liquidus Projections of the Al-Cu-Fe-Si and Al-Cu-Fe-Mg-Si Multicomponent Systems on Al-Rich Side

The thermodynamic calculations of Al–Cu–Fe–Si quaternary system and Al–Cu–Fe–Mg–Si quinary system were carried out using CALPHAD approach based on the Al–Cu–Fe–Mg–Si thermodynamic database. The liquidus surface projection of Al–Cu–Fe–Si quaternary system at the Al-rich corner was constructed, and then the solidification structures of four Al–Cu–Fe–Si alloys were analyzed by the Gulliver-Scheil solidification simulation. The calculated results were in good agreement with the previous experimental data. The liquidus surface projections of A1–Cu–Fe–Mg–Si quinary system at the region of Al-Cu, Al-Si and Al-Mg were constructed, respectively. The liquidus projection of the multicomponent aluminum alloy system at the Al-rich side was accurately drawn, which could accurately predict the primary phase in the solidification process of the alloy. This work has an important guiding significance for the design of the aluminum alloys.

Tribochemical Interaction of Multicomponent Aluminum Alloys During Sliding Friction with Steel

In this work, aluminum multicomponent alloys were studied after friction with steel in a mixed lubrication regime. The resulting secondary structures on the friction surface were investigated by scanning electron microscopy (SEM), energy dispersive analysis (EDX), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction analysis (XRD). In addition to the mass transfer of steel counterbody particles, phase transformations and new chemical compounds formed as a result of interaction with the lubricant were revealed. The release of elements, mainly magnesium and to a lesser extent zinc, from a solid solution of aluminum alloy was also observed, which indicates the occurrence of a non-spontaneous reaction with a negative entropy production.

Prediction models for macro shrinkage of aluminum alloys based on machine learning algorithms

Macro shrinkage percentage is one of the most important measures of the castability of alloys. To quickly and accurately predict the macro shrinkage of aluminum alloys is greatly meaningful before developing new components or new alloys. In this study, based on the dataset of macro shrinkage percentage of aluminum alloys obtained by our experiments, three algorithms, Support Vector Regression (SVR), General Regression Neural Network (GRNN) and Random Forest (RF), were adopted to construct the prediction models for macro shrinkage, respectively. K-fold cross-validation and analysis on stability and reliability of models indicate the RF model is the best one. Compared the prediction values of macro shrinkage percentage in binary, ternary and multicomponent aluminum alloys by RF model with the experiment data of this study and the data from literatures, the constructed model has been verified to have high prediction accuracy and reliability.

Modelling and experimental validation of microstructure evolution during the cooling stage of homogenization heat treatment of Al–Mg–Si alloys

A CALPHAD-coupled multi-component multi-phase Kampmann–Wagner Numerical modelling framework has been adapted and coupled with a homogenization model to predict the competitive nucleation and growth of multi-sized metastable/stable phase particles during the cooling stage of a homogenization heat treatment. The reported model is the continuation of our previous work on the homogenization soaking modelling (Du et al., 2013). It takes the experimentally verified soaking modelling prediction results as the initial microstructural status and predicts the microstructural evolution during the very last step of the homogenization treatment, i.e. the final cooling process. The model has been applied to two Al–Mg–Si alloys: AA6061 and AA6082. The simulation shows a multi-modal MgSi particle size distribution forms due to multiple nucleation events. For the AA6082 alloy the multiple nucleation events occur mainly due to the local micro-chemistry variations along a dendrite while for AA6061 it is due to the two opposite contributions to supersaturation: the positive contribution by the cooling and the negative contribution by the particles' growth. The interaction of intergranular and intragranular MgSi containing particles have been captured, and the partitioning of Mg and Si solutes among the two different sized MgSi particles has been predicted. The model predictions are in reasonable agreement with the microstructure characterization results obtained with Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM) and Electrical Resistivity Measurement on the quenched samples from some dedicated laboratory-scale homogenization heat treatment experiments. It is concluded that the extended KWN modelling framework is applicable to predict the microstructure evolution during the non-isothermal heat treatment of multicomponent aluminium alloys.

The light alloy Calphad databases PanAl and PanMg

Thermodynamic databases for multi-component aluminum alloys, PanAl, and magnesium alloys, PanMg, are reviewed and applications are highlighted. Precipitation simulations by combining thermodynamic and mobility databases for Al and Mg alloys with the PanPrecipitation module of Pandat are also demonstrated. These simulations can serve as virtual experiments to understand the effects of alloy composition and heat treatment condition on the target properties therefore provide guidance for the design of real experiments, save time and reduce cost. For PanAl the focus in this work is on 7xxx alloy and high throughput calculation (HTC) to understand/predict the effects of major and minor alloying elements on the selected properties of an alloy. For PanMg applications are exemplified in a wider spectrum of Mg alloy design and related processing parameters and the usefulness of the CALPHAD modeling tool in Mg technology is demonstrated.

DIAGNOSTICS OF DENDRITE STRUCTURE OF MULTICOMPONENT ALUMINUM ALLOYS

Stages of system analysis and mathematical models for predicting the dendritic structure of multicomponent aluminum alloys are considered. Computer diagnostics of nonequilibrium crystallization is realized by the joint use of the apparatus of computational thermodynamics and means of computational heat transfer for solving problems of computational materials science. The results of modeling the evolution of the dendritic structure are presented with a change in the diffusion intensity in the solid phase from equilibrium conditions to complete suppression.

Lattice mismatch modeling of aluminum alloys

We present a theoretical framework to accurately predict the lattice mismatch between the fcc matrix and precipitates in the multi-component aluminum alloys as a function of temperature and composition. We use a computational thermodynamic approach to model the lattice parameters of the multi-component fcc solid solution and θ′-Al2Cu precipitate phase. Better agreement between the predicted lattice parameters of fcc aluminum in five commercial alloys (206, 319, 356, A356, and A356+0.5Cu) and experimental data from the synchrotron X-ray diffraction (SXD) has been obtained when simulating supersaturated rather than equilibrium solid solutions. We use the thermal expansion coefficient of thermodynamically stable θ-Al2Cu to describe temperature-dependent lattice parameters of meta-stable θ′ and to show good agreement with the SXD data. Both coherent and semi-coherent interface mismatches between the fcc aluminum matrix and θ′ in Al-Cu alloys are presented as a function of temperature. Our calculation results show that the concentration of solute atoms, particularly Cu, in the matrix greatly affects the lattice mismatch.

Computation material science of structural-phase transformation in casting aluminium alloys

Successive stages of computer simulation the formation of the casting microstructure under non-equilibrium conditions of crystallization of multicomponent aluminum alloys are presented. On the basis of computer thermodynamics and heat transfer during solidification of macroscale shaped castings are specified the boundary conditions of local heat exchange at mesoscale modeling of non-equilibrium formation the solid phase and of the component redistribution between phases during coalescence of secondary dendrite branches. Computer analysis of structural - phase transitions based on the principle of additive physico-chemical effect of the alloy components in the process of diffusional - capillary morphological evolution of the dendrite structure and the o of local dendrite heterogeneity which stochastic nature and extent are revealed under metallographic study and modeling by the Monte Carlo method. The integrated computational materials science tools at researches of alloys are focused and implemented on analysis the multiple-factor system of casting processes and prediction of casting microstructure.

Application of Computational Thermodynamics to the Evolution of Surface Tension and Gibbs-Thomson Coefficient during Multicomponent Aluminum Alloy Solidification

Numerical simulation of multicomponent alloy solidification demands accuracy of thermophysical properties in order to obtain a numerical representation as close as possible to the physical reality. Some alloy properties are only seldom found in the literature. In this paper, a solution of Butler’s formulation for surface tension is presented for Al-Cu-Si ternary alloys, allowing the Gibbs-Thomson coefficient to be calculated as a function of Cu and Si contents. The importance of the Gibbs-Thomson coefficient is related to the reliability of predictions furnished by predictive microstructure growth models and of numerical computations of solidification thermal variables that will be strongly dependent on the values of the thermophysical properties adopted in the calculations. A numerical model based on Powell hybrid algorithm and a finite difference Jacobian approximation was coupled with a ThermoCalc TCAPI interface to assess the excess Gibbs energy of the liquid phase, permitting the surface tension and Gibbs-Thomson coefficient for Al-Cu-Si hypoeutectic alloys to be calculated. The computed results are presented as a function of the alloy composition.

Modeling of soft phase transfer to the surface of multicomponent aluminum alloy in friction

A model for calculating the dependence of the amount of solid lubricant formed on the surface of a multicomponent aluminum alloy in friction is proposed. The constructed model is applied to analyze how the properties of the matrix and soft structural components as well as the alloy structure influence the amount of the soft phase transferred to the friction zone which provides for self-lubrication. Recommendations are given concerning the structural composition of the alloy (size and density of phase inclusions) in order to increase the thickness of formed lubricant film in specified friction modes.

Determining hot cracking index of Al–Mg–Zn casting alloys calculated using effective solidification range

The possibility of determining the hot cracking index using the calculated value of the effective solidification range is investigated for multicomponent cast aluminium alloys based on the Al–Mg–Zn system with Mn, Ni, Fe and Si additives. The upper limit of the effective solidification range was calculated as the temperature of formation of a 65 wt-% solid phase using the Sheil model. The linear relationship of the hot cracking index and the effective solidification range in the industrial and experimental multicomponent alloys based on the Al–Mg–(Zn) system is demonstrated.

Modelling of micro- and macrosegregation for industrial multicomponent aluminium alloys

Realistic predictions of macrosegregation formation during casting of aluminium alloys requires an accurate modeling of solute microsegregation accounting for multicomponent phase diagrams and secondary phase formation. In the present work, the stand alone Alstruc model, a microsegregation model for industrial multicomponent aluminium alloys, is coupled with the continuum model ALSIM which calculates the macroscopic transport of mass, enthalpy, momentum, and solutes as well as stresses and deformation during solidification of aluminium. Alstruc deals with multicomponent alloys accounting for temperature dependent partition coefficients, liquidus slopes and the precipitation of secondary phases. The challenge associated with computation of microsegregation for multicomponent alloys is solved in Alstruc by approximating the phase diagram data by simple, analytical expressions which allows for a CPU-time efficient coupling with the macroscopic transport model. In the present work, the coupled model has been applied in a study of macrosegregation including thermal and solutal convection, solidification shrinkage and surface exudation on an industrial DC-cast billet.

Simulation of Dendritic Growth in Multicomponent Aluminium Alloys by Point Automata Method

A numerical model is developed to describe the dendritic growth of multicomponent aluminium alloys, based on a coupled deterministic continuum mechanics heat and species transfer model and a stochastic localized growth model that takes into account the undercooling temperature, curvature, kinetic, and thermodynamic anisotropy. The stochastic model receives temperature and concentration information from the deterministic model and the deterministic heat and species diffusion equations receive the solid fraction information from the stochastic model. The heat and species transfer models are solved on a regular grid by the standard explicit Finite Difference Method (FDM). The dendritic growth model of multicomponent alloy [1,2] is solved by a novel Point Automata (PA) approach [3,4] where the regular cells of the Cellular Automata (CA) method are replaced by the randomly distributed points and neighborhood configuration, similar as appears in meshless methods. The PA method was developed in order to circumvent the mesh anisotropy problem, associated with the classical CA method. The present paper extends our previous developments of Pa method to multicomponent alloys. A comparison of the results, obtained by the PA and CA method is shown for Al-5.3% Zn-2.35% Mg-1.35% Cu-0.5% alloy.

Prediction of As-Cast Grain Size of Inoculated Multicomponent Aluminum Alloys

In this paper, an extendedMaxwell-Hellawell numerical grain size prediction model is employed to predictas-cast grain size of inoculated aluminum alloys. Given melt composition,inoculation and cooling conditions, the model is able to predict maximumnucleation undercooling, cooling curve and final as-cast grain size of multi-componentalloys. The proposed model has been applied to various binary andmulticomponent alloys. Upon analyzing the numerical simulation results, it isfound that for both binary and multi-component alloys, grain size does not havea one-to-one relation with Growth Restriction Factor, Q, but has a clear ubiquitous correlation with the average diffusivity-weightedQ, defined as W in this paper. This founding helps solve the controversy seen inthe recent work on analytical grain size and Q relations. It also has been used to interpret the scatters seenin the measured grain sizes as a function of Q values reported in the literature.

An extension of the Kampmann–Wagner numerical model towards as-cast grain size prediction of multicomponent aluminum alloys

The Kampmann–Wagner numerical (KWN) model, which has been widely adopted as a precipitation modeling framework accounting for concurrent nucleation, growth and coarsening kinetics, was extended to predict the as-cast grain size of inoculated multicomponent aluminum alloys. In the model, the heterogeneous nucleation of grains on inoculant particles was modeled based on the free growth criterion, while the influence of the solute on the nucleation behavior, in terms of the solute suppressed nucleation (SSN) effect, was rigorously defined and integrated. In order to fully address the solidification behavior of multicomponent alloys, a coupling of the KWN model to CALPHAD was carried out. These extensions allow the treatment of two different nucleation-ceasing mechanisms induced by grain growth: recalescence stifling and solute segregation stifling. Given melt composition, inoculation and heat extraction rate, the model is able to predict maximum nucleation undercooling, cooling curve and the final as-cast grain size of multicomponent alloys without invoking the binary equivalence assumption used in the existing models. The proposed model was tested with a variety of binary and multicomponent aluminum alloys, and the predictions were compared with the experimental measurement results and previous grain size prediction models. The simulation results show that the SSN effect has a negligible influence on the nucleation behavior and the final grain size during isothermal melt solidification, but a strong influence on the ceasing of grain nucleation during directional solidification. Reasonable agreement was obtained between the model prediction and measurement results on a direct chill casting experiment of an AA5182 alloy. Our work proves that the application of the precipitation modeling framework for the solidification problem is successful.

Determining hot cracking index of Al–Si–Cu–Mg casting alloys calculated using effective solidification range

The possibility of determining the hot cracking index using the calculated value of the effective solidification range is investigated for multicomponent cast aluminium alloys based on the system Al–Si–Cu–Mg with Mn, Ni, Fe and Zn additives. The upper limit of the effective solidification range was calculated as the temperature of formation of 65 wt-% solid phase using Sheil model. The linear relationship of the hot cracking index and the effective solidification range in the industrial and experimental multicomponent alloys based on the Al–Si–Cu–Mg system is demonstrated.