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The Various Uses of TCAL7 TCS Al-based Alloy Database

The versatile TCS Al-based Alloy Database (TCAL7) is developed for aluminum-based alloys. It can be applied, but is not limited, to the industrial grades. Essentially a thermodynamic database, it also has thermophysical properties data available for molar volume with thermal expansion coefficients, electrical resistivity and thermal conductivity of all phases, as well as viscosity and surface tension of liquid. Dr. Hai-Lin Chen, Database Developer at Thermo-Calc Software, describes the multiple advantages of TCAL7 in this blog post.


Dr. Hai-Lin Chen of Thermo-Calc Software has
extensive experience in database development
for aluminum-based alloys. He is part of the team
who developed TCAL7.

A Thermodynamic Database with Thermophysical Properties

TCAL7 can be used as a conventional CALPHAD thermodynamic database for predicting phase formation, phase amounts and phase compositions during casting and heat treatments. Additionally, one can use it to calculate various thermodynamic properties, such as heat capacity, energy of formation, reaction heat and driving force.

Together with the compatible atomic TCS Aluminum Mobility Database (MOBAL5), TCAL7 can be used for kinetic simulations. With the Diffusion Module (DICTRA), one can simulate diffusion-controlled transformations, for instance for studying homogenization of matrix phase and dissolution of grain boundary particles. TCAL7 and MOBAL5 can also simulate concurrent nucleation, growth and coarsening during multi-particle precipitation with the Precipitation Module (TC-PRISMA). One can generate important data, such as critical radius of nuclei, particle size distribution, number density and their evolution. These kinds of simulations are perfect for designing aging treatments and in principle may also be employed to the early stage of solidification.


Aluminum is widely used in a host of industries, from building and construction, transportation, automobile, aerospace to household appliances. TCAL7 is a versatile database which enables cal-culations and simulations for aluminum alloys composition design and process design.

TCAL7 supports calculating the aforementioned thermophysical properties and deriving properties related to them, either for individual phases or for specific alloys. This provides more necessary data for material design and process optimization. For instance, the molar volume data allow us to evaluate alloy density, casting shrinkage, lattice mismatch of a precipitate with the matrix phase. Thermal conductivity can be used for deriving thermal resistivity and thermal diffusivity, and electrical resistivity for electrical conductivity. As an example, thermal conductivity is one of the key factors which is needed for optimizing additive manufacturing.


Electrical resistivity (ELRS) are calculated for 35 wrought Al alloys after “O” heat treatments and compared with tabulated data (NDT Education, March 2002). A calibration is made for interface scattering and assumed to be proportional to the total fraction of grain boundary phases with a coefficient of +4.83e-8 ohm.m. The red solid line indicates where calculated values are equal to experimental data. The green and blue dashed lines mark the limits for 10 % and 20% deviations, respectively.

Aluminum Alloy Composition Design and Process Design for Industry and Academia

TCAL7 is developed for aluminum alloys of industrial relevance, from 1000 series to 8000 series. Anyone working on aluminum alloy composition design and process design would benefit from using TCAL7.


Equilibrium calculation for an AA6005 alloy (Al-0.82Si-0.55Mg-0.016Cu-0.5Mn-0.2Fe, wt. %). This helps to make a preliminary determination of the solution treating temperature, somewhere Mg2Si is not stable and the alloy does not melt.

Aluminum Alloy Casting

TCAL7 and its predecessors have been widely employed to aluminum alloys casting simulations. As a quick investigation, an equilibrium stepping calculation and a Scheil simulation provide the upper and lower boundaries of the solidification interval, and a real solidification under normal conditions is expected to fall between them. If the cooling rate is known, or can be estimated, Scheil simulations can be performed in a more accurate way, considering the diffusion in solid phases with the back-diffusion module. Solidification simulations predict not only the formation of grain boundary phases, but also the extent of composition segregation in the (Al) grains. To some extent, the information on phase transformation even allows us to interpret microstructure formation in as-cast alloys and to evaluate their castability, such as hot tearing susceptibility.


Solidification simulations for an AA7075 alloy (0.2 Si, 0.25 Fe, 1.6 Cu, 0.15 Mn, 2.5 Mg, 5.6 Zn, wt.%). Three methods are used here: Conventional Scheil, Scheil with back diffusion at 0.7 K/s and equilibrium stepping. The terminal freezing range is evaluated for the simulation with back diffusion. The large value indicates high hot tearing susceptibility of this types of alloys.

Benefits of TCAL7 in Various Heat Treatments

Solution Treatment

The heating temperature can be provisionally determined with a stepping calculation. The Diffusion Module (DICTRA) simulations can be run with TCAL7 and MOBAL5 for dissolution of particles. Such simulations help to optimize the heating temperature, and to estimate the heating time which is needed for the particles to be fully dissolved into the (Al) matrix, or to reach the maximum extent of the solution. Diffusion Module (DICTRA) simulations can also be performed to approximate the particle size distribution. The initial particle fraction can be either measured with experiments or estimated with solidification simulations.


Simulated dissolution of Si particles at 500 °C, 530 °C, and 560 °C with a multiple-cell approach for approximating the size distribution. 500 °C is too low since the particles cannot be fully dissolved even after 3 h. By comparison, the particles will disappear within 15 min at 560 °C and 1 h at 530 °C. One can choose either temperature or a temperature between taking into account other factors, such as energy consumptions, risks of melting and so forth.


Alloy AA7093 uncertainty calculation using the Property Model Calculator. Transition temperatures are calculated at 200 compositions within the specification tolerance (for example Zn 10.3±0.5, Cu 1.6±0.1, Mg 2.0±0.1, wt.%) and the frequency of each obtained temperature is plotted. The composition variation narrows down the single-(Al)-phase region compared to the nominal composition. The optimal heating temperature can thus be quickly determined at 468 °C in this case, although in most cases Diffusion Module (DICTRA) simulations are needed for optimizing the temperature.

Homogenization Treatment

The Diffusion Module (DICTRA) simulations can provide detailed composition profiles for the (Al) grains at specific time during the homogenization, whether the heating is isothermal, isochronal, stepwise or even more complex procedures. They help to optimize the heating temperature and estimate the holding time. The initial composition profile can be either measured with experiments or estimated with simulations.


DICTRA simulations of the dissolution of Mg2Si particles and elimination of composition segregation in (Al) grains in an AA6005 alloy (Al-0.82Si-0.55Mg-0.016Cu-0.5Mn-0.2Fe, wt. %). The initial Mg2Si radius is set at 0.6 micron. The initial Mg2Si fraction and the (Al) compositions were from a conventional Scheil simulation without considering back diffusion.

Aging Treatments

Most important metastable precipitates in aluminum alloys have been modeled in TCAL7. TCAL7 and MOBAL5 can be used with the Precipitation Module (TC-PRISMA) to simulate multi-particle precipitation during aging. This provides for example the evolution of particle size distribution and number density with the aging time. The simulations can be run by considering aspect ratio, so that the morphology change may be tentatively predicted.


A precipitation simulation during the aging treatment (heating procedure: from 20 °C to 120 °C at 30 °C/h, 30 °C for 6 h, from 120 °C to 135 °C at 15 °C/h, and remains at 135 °C) showing the solutes contents in the η’ precipitates in the 7093 alloy. η’ is a major strengthening precipitate in the 7000 series of alloys. Curves are from the Precipitation Module (TC-PRISMA) simulations and symbols are experimental data from (Marlaud, Acta Mater. 58 (2010) 248).

Recycling of Aluminum Scrap

As demonstrated in the publication Development and applications of the TCAL aluminum alloy database, it helps to choose the recycling route and design the recycling process of specific types of scrap. One of the highlights in those examples is that CALPHAD calculations can easily predict the Fe removal fraction. It can quickly optimize the processing temperature and the addition of alloying element.


Calculated Al-Fe-Mn isopleth at 9.5 Si and 1.6 Fe (arbitrary composition for aluminum scraps which typically contain high amounts of Si and Fe) and varying Mn (in wt.%). This is to design the recycling process of removing Fe by adding Mn and forming Fe-containing α-Al15Si2Mn4 particles. The processing temperature and the Mn content are suggested as indicated in the plot. Lowering the temperature increases the amount of α and reduces energy consumption, but the formation of (Al) grains should be avoided. Adding more Mn further increases the formation of Al15Si2Mn4 and Fe removal fraction, while it increases the cost as well.

The TCAL7 TCS Al-based Alloy Database is available with a host of calculation and validation examples. For further information, visit our Aluminum-based Alloys Databases page.

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