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High-throughput thermodynamic calculations of phase equilibria in solidified 6016 Al-alloys
Computational Materials Science 167 (2019) 19–24
Contents lists available at ScienceDirect
Computational Materials Science journal homepage: www.elsevier.com/locate/commatsci
High-throughput thermodynamic calculations of phase equilibria in solidified 6016 Al-alloys
T
⁎
Cong Zhang (Ph.D.)a, , Xue Jianga,b, Ruijie Zhanga,b, Xin Wanga, Haiqing Yina,b, Xuanhui Qub,c, Zi-Kui Liud a
Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, PR China Beijing Key Laboratory of Materials Genome Engineering, Beijing 100083, PR China c Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, PR China d Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA b
A R T I C LE I N FO
A B S T R A C T
Keywords: 6016 High-throughput Scheil-Gulliver Phase equilibria CALPHAD
In the present work, high-throughput calculation (HTC) method is performed to obtain the phase equilibria of solidified 6016 Al-alloys. The calculations of primary phase fraction, precipitates fraction and phase composition are realized based on the Scheil-Gulliver solidification model of Thermo-Calc software, and the entire composition ranges of standard 6016 Al-alloy is taken into account. A Python-based program called Automatic Execution and Extraction Tasks (AEET) is developed, it automatically generate the commands of calculations, execute the Thermo-Calc software and then extract the key data of output files. The obtained results are listed in an Excel file, which is convenient for the subsequent visualization analysis and machine learning. Several criteria are combined to filter the appropriate compositions of industrial 6016 Al-alloys, providing a valuable guidance to the experimentalists and avoiding unnecessary trial-and-error tests. This HTC approach is not limited to the solidification modelings; it can be extended to any kinds of thermodynamic and kinetic calculations.
1. Introduction The 6xxx series Al-Mg-Si alloys are widely used in the fields of automotive and aircraft applications due to their lightweight, high strength, excellent formability and outstanding corrosion resistance [1–3]. The manufacture of 6xxx series aluminum alloys contains the process of casting, homogenization, hot/cold rolling, solution treatment and natural/artificial aging. The precipitates evolution during aging process follows the sequence of GP zones → β′′→β′/U1/U2 → Mg2Si, while the β′′ is the major strengthening phase in 6xxx series aluminum alloys [4]. Since the final mechanical property of aluminum alloys is correlated to the whole production process, in which casting is one of the important steps, thus the detailed investigation of casting phase equilibria is beneficial to the quality control in the subsequent steps. As the composition range for standard aluminum alloys is rather wide and the experimental determinations of the casting microstructure involve complex procedures, causing the possible number of composition sets to test is immense and costly for aluminum-making company. Therefore, an efficient high-throughput calculation method to select proper casting alloys is of great value. In the present work, the phase equilibria of solidified 6016 Al-alloys
⁎
within the entire standard composition ranges, such as primary phase fraction, precipitates fraction and phase composition, were calculated by means of CALPHAD (CALculation of PHAse Diagrams) approach, which is a useful tool to develop and design novel materials [5–7]. A Python-based program is developed to perform the automatic calculation and results extraction, as is indispensable for the high-throughput thermodynamic calculation technique, which is also a constituent of the Ocean of Data infrastructure [8]. In addition, key criteria are combined to filter the appropriate compositions of industrial 6016 Al-alloys, providing a valuable guidance to the experimentalists and reducing unnecessary trial-and-error tests. 2. Model description The standard composition of 6016 alloy [9] as well as the selected compositions for calculations are listed in Table 1. Since Mg and Si are the major components to form strengthening precipitates, the composition step for Mg and Si is defined as 0.05 wt%, which is sufficient low to meet the manufacture accuracy of industrial aluminum alloys. The Cu is another important additive, however it is not emphasized in the standard 6016 alloy grade, in the present work the composition of Cu is
First author and Corresponding author at: 30 Xueyuan Road, Haidian District, Beijing 100083, PR China. E-mail address: [email protected] (C. Zhang).
https://doi.org/10.1016/j.commatsci.2019.05.022 Received 5 April 2019; Received in revised form 11 May 2019; Accepted 12 May 2019 Available online 18 May 2019 0927-0256/ © 2019 Elsevier B.V. All rights reserved.
Computational Materials Science 167 (2019) 19–24
C. Zhang, et al.
Table 1 The standard composition of 6016 alloy and the selected compositions for calculations. Component
Al
Si
Mg
Fe
Cu
Mn
Zn
Cr
Ti
Others
Standard, wt% Selected, wt%
Bal. Bal.
1.00–1.50 Δ = 0.05a
0.25–0.60 Δ = 0.05
≤0.50 0.40
≤ 0.20 0.10, 0.20
≤0.20 0.10
≤0.20 0.15
≤0.10 0.05
≤0.15 0.10
≤0.15 0.00
a
Δ = 0.05 is the composition step within the standard composition interval.
selected to be 0.1 and 0.2 wt% for calculations. The other components are impurities such as Fe, Mn, Zn, Cr, Ti, and their composition are placed in moderate values. Based on the above selections, a total number of 176 sets of alloy composition are created. To obtain the primary phase fraction, precipitates fraction and phase composition of 6016 alloy, the thermodynamic calculation based on the Scheil-Gulliver model [10,11] was performed via Thermo-Calc software [12] employing the COST507 thermodynamic database [13]. Since the 6016 is a classic low-concentrated Al-alloy grade and mainly based on Al-Mg-Si system, the COST507 database was validated to be sufficient to calculate the phase equilibria reasonably [14–16]. In the Scheil-Gulliver model, several assumptions were proposed, such as no diffusion in the solids and complete diffusion in the liquid. The calculated Scheil-Gulliver solidification diagram and solidified phase fraction for 6016 alloy are shown in Fig. 1(a)–(b), with the composition of Al-1.1Si-0.5Mg-0.4Fe-0.2Cu-0.1Mn-0.15Zn-0.05Cr-0.1Ti as a case, in which the phase equilibria information can be obtained accordingly. The directional solidification and semi-continuous casting are normally performed for industrial aluminum alloys, the initial transient, steady-state and final transient solidification layers are existed in the casting ingot [17]. The initial transient is a composition macro-segregation layer, while the steady-state layer is homogeneous and beneficial to the subsequent processes. The establishment of a steady-state boundary layer requires a distance of growth which corresponds to the length of the initial transient, and can be described by the equation below [17]:
C k ·h·V ⎞ ⎤ CL* = ⎛ 0 ⎞ ⎡1 − (1 − k ) exp ⎛− ⎢ k D ⎠⎥ ⎝ ⎠⎣ ⎝ ⎦
(1)
CS* = k ·CL*
(2)
Fig. 2. General infrastructure of the high-throughput calculation program AEET. Using thermodynamic parameters from given database, and the output data together with other criteria are used to screen the target compositions.
In the present work, the amount of α-AlFeSi and β-AlFeSi harmful phases as well as the length of initial transient layer are the key factors to screen the reasonable composition sets of 6016 aluminum alloys. 3. HTC infrastructure
in which CL* is the liquid composition at solid/liquid interface, CS* is the solid composition at the solid/liquid interface, C0 is the alloy nominal composition, k is the equilibrium distribution coefficient, h is the solidified length, V is the crystallization velocity (m/s) and D is the diffusion coefficient in liquid phase (m2/s). Eq. (1) permits an estimation of the length that must solidify before the steady state is reached.
The calculation of single Scheil-Gulliver solidification task can be performed by commands typing or TCM file execution for users of Thermo-Calc software. However, it is low efficient and time-consuming to accomplish the calculations of 176 composition sets of 6016 aluminum alloys, therefore the high-throughput calculation (HTC) method is developed. The HTC infrastructure is schematically shown in Fig. 2, it
Fig. 1. The calculated (a) Scheil-Gulliver solidification diagram and (b) solidified phase fraction for 6016 alloy with the composition of Al-1.1Si-0.5 Mg-0.4Fe-0.2Cu0.1Mn-0.15Zn-0.05Cr-0.1Ti. 20
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Fig. 3. The Python coding for the (a) generation of TCM files and (b) results file extraction, which are integrated into AEET program.
the next section.
consists of Input layer, Execution layer and Output layer. For the Input layer, the alloy composition range and composition step are defined, while the thermodynamic database should be placed here. For the Execution layer, the Generate TCM file, Execute ThermoCalc and Extract results file are the three major steps. In the step of ‘Generate TCM file’, the calculation commands files are generated based on the selected 176 composition sets of 6016 aluminum alloys. These files are Thermo-Calc macro (TCM) format that contain alloy composition, initial temperature, stable phases and output items. The generation of TCM files can be performed by a Python coding shown in Fig. 3(a), by which the 176 TCM files corresponding to the 176 composition sets are generated. To speed up the running efficiency of Thermo-Calc software, the 176 files are integrated into one file. In the step of ‘Execute Thermo-Calc’, the Thermo-Calc was switched on when detected the appearance of TCM file, and will execute the commands within it. After which, the 176 sets of results files for the primary phase fraction, precipitates fraction and phase composition of 6016 alloy can be obtained. In the step of ‘Extract results file’, the calculated results are extracted from the output files (EXP files). Since the primary phase fraction, precipitates fraction and phase composition data are stored with logical rules defined by Thermo-Calc, the required data can be collected by following it. The Python coding for this function is displayed in Fig. 3(b). For the Output layer, all the extracted data are stored in Excel-type file, which is convenient for the subsequent analytical processing. To realize the above HTC tasks, a Python-based program called Automatic Execution and Extraction Tasks (AEET) is developed, which integrated the Input layer, Execution layer and Output layer. The AEET can generate the commands of calculations, execute the Thermo-Calc software and then extract the key data of output files automatically. In the present work, the amount of precipitates, especially α-AlFeSi and βAlFeSi harmful phases, together with other key criteria are used to screen the appropriate alloy compositions, which will be discussed in
4. Results and discussion The massive phase equilibria data of 6016 aluminum alloy are obtained via HTC approach; it can be used to analyze the influence of alloy composition on the primary phase fraction, solidified phase fraction as well as phase composition. In addition, the target compositions can be screened based on the above phase equilibria information, which is the subsequent work after AEET, as shown in Fig. 2. 4.1. High-throughput calculation results The fraction of primary phase (Al), strengthening phase Mg2Si and harmful phases α-AlFeSi, β-AlFeSi after solidification of 6016 Al-alloys are the major concerns on industrial application. In the present work, the Cu content has values of 0.1 and 0.2 wt%, since it do not change the trend of phase fraction evolution during solidification, it is representative to show the results with Cu content of 0.1 wt%. To facilitate reading, these HTC results are presented in contour map forms and shown in Fig. 4(a)–(d). As can be seen from Fig. 4(a), the fraction of primary phase (Al) is enriched in the low-Mg and low-Si place. This is due to the (Al) is mainly consisted of Al with minor amount of Mg, Si and other elements. The higher concentrated solute atoms is able to prompt the formation of precipitates such as Mg2Si and AlFeSi phases, and thus lowered the primary phase fraction. Besides, it also makes the high-Mg and high-Si region has larger amount of Mg2Si phase, as shown in Fig. 4(b). The composition of α-AlFeSi and β-AlFeSi phases are close to the stoichiometry of Al12Fe3Si2 and Al5FeSi, respectively [18–20]. Therefore, the β-AlFeSi phase has higher Si mole fraction than α-AlFeSi, and enriched in the high-Si corner, whereas α-AlFeSi phase enriched in the low-Si corner, which is in accordance with Fig. 4(c)–(d). It could be noted that the Mg content has effect on the formation of α-AlFeSi and β21
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Fig. 4. The calculated fraction of (a) primary phase (Al), (b) strengthening phase Mg2Si, (c) harmful phase α-AlFeSi and (d) β-AlFeSi after the solidification of 6016 Al-alloys.
Fig. 5. The calculated (a) phase equilibria of 6016 alloy at 300 °C in the Al-rich corner and (b) the solubility limits of Mg + Si in matrix (Al) phase. The calculations are performed by Thermo-Calc software using COST507 database.
AlFeSi phases, the increase of Mg leads to the increase of α-AlFeSi phase and decrease of β-AlFeSi phase.
target compositions. In the first cycle, the solubility limits of solute atoms in (Al) phase is considered. The phase equilibria of 6016 alloy at 300 °C and in the Alrich corner are calculated by Thermo-Calc software using COST507 database, as displayed in Fig. 5(a). The (Al) phase is coexisted with αAlFeSi and β-AlFeSi phases, which is due to the high Fe content (0.4 wt %) used in this calculation. Since the strengthening phase Mg2Si should be dissolved into the matrix phase before artificial aging for 6xxx series aluminum alloys, the solubility limits of Mg + Si in (Al) phase is
4.2. Alloy composition screening The appropriate 6016 alloy compositions can be reversely designed based on the phase equilibria information and other criteria, including solubility limits, Mg/Si/Cu amount, α/β-AlFeSi phase fraction and initial transient length. Hence, four cycles are involved to screen the 22
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Fig. 6. The composition screening of 6016 alloys based on the criteria of (a) solubility limits, (b) Mg/Si/Cu amount, (c) α/β-AlFeSi phase fraction and (d) initial transient length. The sphere symbols indicate the data are included in the next cycle, while the cube symbols are excluded.
Table 2 The selected 20 appropriate compositions for the development of industrial 6016 alloy.
Fig. 7. The calculated equilibrium distribution coefficients for Mg and Si of 6016 Al-alloys.
calculated and shown in Fig. 5(b). As can be seen, the matrix (Al) phase can dissolve more solute atoms at higher temperatures until the eutectic reaction take place, which is Liquid→(Al) + Mg2Si+(Si) at 557 °C. Based on the present calculations, the solubility limits of (Al) at 557, 500, 400 and 300 °C are 1.97, 1.19, 0.41 and 0.10 wt%, respectively. The above calculated results are in good agreement with the
No.
Al
Si
Mg
Fe
Cu
Mn
Zn
Cr
Ti
Others
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal.
1.20 1.20 1.25 1.25 1.25 1.25 1.25 1.30 1.30 1.30 1.35 1.20 1.25 1.25 1.25 1.25 1.25 1.30 1.30 1.35
0.45 0.50 0.40 0.45 0.50 0.55 0.60 0.50 0.55 0.60 0.55 0.50 0.40 0.45 0.50 0.55 0.60 0.55 0.60 0.55
0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40
0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20
0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15
0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
experimental determinations by Ji et al. [21], indicating the reasonable phase equilibria are obtained by thermodynamic calculations. Considering the effect of impurities, the solubility limits cannot reach the 23
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aluminum alloys, but has potentials to many kinds of high-throughput thermodynamic and kinetic calculations.
ideal maximum value of 1.97 wt%, therefore the criteria is set to be 1.90 wt%, and the Mg + Si amount higher than this value are excluded from the composition sets, as depicted in Fig. 6(a). In the second cycle, the solid solution strengthening and dispersion strengthening effects contributed from the solute atoms and precipitates are considered. The strengthening phase of 6016 aluminum alloy is mainly needle-shaped β′′. The excess of Si can promote the precipitation of β′′ with uniform distribution, and the microstructure is refined to achieve higher strength [22]. Therefore the automotive body panel aluminum alloy currently used in industry usually contains excess Si. For the strengthening phase β′′, the Si/Mg mass ratio is 0.92, and the screening standard is set to be w(Si)/w(Mg) > 0.92. The solid solution strengthening is mainly from the solute atoms of Mg, Si and Cu. Based on the experimental work by Simões et al. [23], for w(Mg) + w(Si) + w (Cu) larger than 1.50 wt% the aluminum alloy exhibits better strength property, and the w(Mg) + w(Si) + w(Cu) > 1.50 wt% is another selected criteria, as shown in Fig. 6(b). In the third cycle, the fraction of precipitated α-AlFeSi and β-AlFeSi phases are taken into consideration. During the homogenization of 6xxx series aluminum alloy, the α-AlFeSi and β-AlFeSi phases are brittle and act as fracture sources. Due to the complex crystal structure of the AlFeSi phases, the atomic diffusion rate is slow, and it takes longer time to dissolve into the matrix during the homogenization process [24]. If the AlFeSi phases are not fully dissolved, it is unfavorable for the subsequent rolling process. The fraction of α-AlFeSi and β-AlFeSi phases is displayed in Fig. 6(c), and the composition sets for the lower half of AlFeSi phase fraction are selected and transferred to the next cycle. In the last cycle of the present work, the length of initial transient macro-segregation layer is evaluated. To calculate the length of initial transient layer, the Eq. (1) described in Section 2 is utilized. Since the crystallization velocity V is a fixed processing parameter during manufacture and the diffusion coefficient D has minor variation within the 6016 standard composition ranges, these two values can be reduced. Then the length is correlated with equilibrium distribution coefficient k and nominal composition. The calculated characteristic distance required to establish a steady-state planar interface having lower limit of the standard composition [17] is shown in Fig. 6(d). As the length of segregation for Si is larger than that of Mg, only the results for Si is displayed in the figure. The distribution coefficient k used in the above calculation is obtained by thermodynamic calculations as shown in Fig. 7, in which the Thermo-Calc software and COST507 database are applied. Based on all the above calculations, the final 20 compositions are selected and listed in Table 2, which are promising composition sets to be attempted of developing 6016 industrial alloys. The reliable and applicable of COST507 database ensured the calculated phase equilibria can be used for composition screening and materials selection of 6016 alloys.
CRediT authorship contribution statement Cong Zhang: Formal analysis, Validation, Visualization, Writing original draft, Writing - review & editing. Xue Jiang: Conceptualization, Methodology. Ruijie Zhang: Investigation. Xin Wang: Data curation, Software. Haiqing Yin: Methodology, Project administration. Xuanhui Qu: Project administration. Zi-Kui Liu: Validation. Acknowledgements The financial supports from the National Key Research and Development Program of China (Grant No. 2016YFB0700503), National Natural Science Foundation of China (Grant No. 51701013) and Fundamental Research Funds for the Central Universities (Grant No. FRF-TP-17-005A1) are greatly acknowledged. References [1] W.S. Miller, L. Zhuang, J. Bottema, A.J. Wittebrood, P. De Smet, A. Haszler, A. Vieregge, Recent development in aluminum alloys for the automotive industry, Mater. Sci. Eng. A 280 (2000) 37–49. [2] Y. Birol, Pre-aging to improve bake hardening in a twin-roll cast Al-Mg-Si alloy, Mater. Sci. Eng. A 391 (2005) 175–180. [3] W.J. Liang, P.A. Rometsch, L.F. Cao, N. Birbilis, General aspects related to the corrosion of 6xxx series aluminium alloys: exploring the influence of Mg/Si ratio and Cu, Corros. Sci. 76 (2013) 119–128. [4] C.D. Marioara, S.J. Andersen, H.W. Zandbergen, R. Holmestad, The influence of alloy composition on precipitates of the Al-Mg-Si system, Metall. Mater. Trans. A 36 (2005) 691–702. [5] N. Li, W. Zhang, Y. Du, W. Xie, G. Wen, S. Wang, A new approach to control the segregation of (Ta,W)C cubic phase in ultrafine WC-10Co-0.5Ta cemented carbides, Scr. Mater. 100 (2015) 48–50. [6] C. Zhang, H. Yin, R. Zhang, X. Jiang, G. Liu, Y. Du, Experimental and thermodynamic investigation of gradient zone formation for Ti(C,N)-based cermets sintered in nitrogen atmosphere, Ceram. Int. 43 (2017) 12089–12094. [7] R. Jha, N. Chakraborti, D.R. Diercks, A.P. Stebner, C.V. Ciobanu, Combined machine learning and CALPHAD approach for discovering processing-structure relationships in soft magnetic alloys, Comput. Mater. Sci. 150 (2018) 202–211. [8] Z.-K. Liu, Ocean of data: integrating first-principles calculations and CALPHAD modeling with machine learning, J. Phase Equilib. Diffus. 39 (2018) 635–649. [9] International Alloy Designations and Chemical Composition Limits for Wrought Aluminum and Wrought Aluminum Alloys, The Aluminum Association, Arlington, VA, 2009. [10] G.H. Gulliver, The quantitative effect of rapid cooling upon the constitution of binary alloys, J. Inst. Met. 9 (1913) 120–157. [11] E. Scheil, Unbroken series of solid solutions in the binary systems of the elements, Z. Metallkd. 34 (1942) 242–246. [12] B. Sundman, B. Jansson, J.O. Andersson, The thermo-calc databank system, Calphad 9 (1985) 153–190. [13] I. Ansara, COST 507-Thermochemical Database for Light Metal Alloys, European Commission, Brussels/Luxembourg, 1995. [14] E. Balitchev, T. Jantzen, I. Hurtado, D. Neuschutz, Thermodynamic assessment of the quaternary system Al-Fe-Mn-Si in the Al-rich corner, Calphad 27 (2003) 275–278. [15] Y. Tang, Y. Du, L. Zhang, X. Yuan, G. Kaptay, Thermodynamic description of the Al-Mg-Si system using a new formulation for the temperature dependence of the excess Gibbs energy, Thermochim. Acta 527 (2012) 131–142. [16] H.-L. Chen, Q. Chen, A. Engstroem, Development and applications of the TCAL aluminum alloy database, Calphad 62 (2018) 154–171. [17] W. Kurz, D.J. Fisher, Fundamental of Solidification, Trans Tech Publications Ltd, Switzerland, 1998 Fourth Revised Edition. [18] J.M. Yu, N. Wanderka, G. Miehe, J. Banhart, Intermetallic phases in high purity Al-10Si0.3Fe cast alloys with and without Sr modification studied by FIB tomography and TEM, Intermetallics 72 (2016) 53–61. [19] N. Bayat, T. Carlberg, M. Cieslar, In-situ study of phase transformations during homogenization of 6005 and 6082 Al alloys, J. Alloy. Compd. 725 (2017) 504–509. [20] R. Kumar, A. Gupta, A. Kumar, R.N. Chouhan, R.K. Khatirkar, Microstructure and texture development during deformation and recrystallization in strip cast AA8011 aluminum alloy, J. Alloy. Compd. 742 (2018) 369–382. [21] Y. Ji, H. Zhong, P. Hu, F. Guo, Use of thermodynamic calculation to predict the effect of Si on the ageing behavior of Al-Mg-Si-Cu alloys, Mater. Des. 32 (2011) 2974–2977. [22] A.K. Gupta, D.J. Lloyd, S.A. Court, Precipitation hardening in Al-Mg-Si alloys with and without excess Si, Mater. Sci. Eng. A 316 (2001) 11–17. [23] V. Simões, H. Laurent, M. Oliveira, L. Menezes, The influence of warm forming in natural aging and springback of Al-Mg-Si alloys, Int. J. Mater. Form. 12 (2019) 57–68. [24] G. Sha, K. O'Reilly, B. Cantor, Characterization of Fe-Rich intermetallic phases in a 6xxx series Al alloy, Mater. Sci. Forum. 519–521 (2006) 1721–1726.
5. Conclusion The appropriate 6016 alloy compositions are reversely designed based on the high-throughput calculation (HTC) method and ScheilGulliver model, the primary phase fraction, precipitates fraction and phase composition are calculated for the entire composition ranges of standard 6016 Al-alloy. A Python-based program AEET (Automatic Execution and Extraction Tasks) is developed to improve the calculation efficiency, which can generate the commands of calculations, execute the Thermo-Calc software and then extract the key data of output files automatically. The fraction of harmful α/β-AlFeSi phases together with other criteria, such as solubility limits, Mg/Si/Cu amount and initial transient length, are used to filter the appropriate compositions of industrial 6016 Al-alloys. This method provides a valuable guidance to the experimentalists, for the time-consuming and unnecessary trial-and-error tests can be reduced. Moreover, the present HTC approach is not limited to the solidification calculations for 6016 24
Contents lists available at ScienceDirect
Computational Materials Science journal homepage: www.elsevier.com/locate/commatsci
High-throughput thermodynamic calculations of phase equilibria in solidified 6016 Al-alloys
T
⁎
Cong Zhang (Ph.D.)a, , Xue Jianga,b, Ruijie Zhanga,b, Xin Wanga, Haiqing Yina,b, Xuanhui Qub,c, Zi-Kui Liud a
Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, PR China Beijing Key Laboratory of Materials Genome Engineering, Beijing 100083, PR China c Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, PR China d Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA b
A R T I C LE I N FO
A B S T R A C T
Keywords: 6016 High-throughput Scheil-Gulliver Phase equilibria CALPHAD
In the present work, high-throughput calculation (HTC) method is performed to obtain the phase equilibria of solidified 6016 Al-alloys. The calculations of primary phase fraction, precipitates fraction and phase composition are realized based on the Scheil-Gulliver solidification model of Thermo-Calc software, and the entire composition ranges of standard 6016 Al-alloy is taken into account. A Python-based program called Automatic Execution and Extraction Tasks (AEET) is developed, it automatically generate the commands of calculations, execute the Thermo-Calc software and then extract the key data of output files. The obtained results are listed in an Excel file, which is convenient for the subsequent visualization analysis and machine learning. Several criteria are combined to filter the appropriate compositions of industrial 6016 Al-alloys, providing a valuable guidance to the experimentalists and avoiding unnecessary trial-and-error tests. This HTC approach is not limited to the solidification modelings; it can be extended to any kinds of thermodynamic and kinetic calculations.
1. Introduction The 6xxx series Al-Mg-Si alloys are widely used in the fields of automotive and aircraft applications due to their lightweight, high strength, excellent formability and outstanding corrosion resistance [1–3]. The manufacture of 6xxx series aluminum alloys contains the process of casting, homogenization, hot/cold rolling, solution treatment and natural/artificial aging. The precipitates evolution during aging process follows the sequence of GP zones → β′′→β′/U1/U2 → Mg2Si, while the β′′ is the major strengthening phase in 6xxx series aluminum alloys [4]. Since the final mechanical property of aluminum alloys is correlated to the whole production process, in which casting is one of the important steps, thus the detailed investigation of casting phase equilibria is beneficial to the quality control in the subsequent steps. As the composition range for standard aluminum alloys is rather wide and the experimental determinations of the casting microstructure involve complex procedures, causing the possible number of composition sets to test is immense and costly for aluminum-making company. Therefore, an efficient high-throughput calculation method to select proper casting alloys is of great value. In the present work, the phase equilibria of solidified 6016 Al-alloys
⁎
within the entire standard composition ranges, such as primary phase fraction, precipitates fraction and phase composition, were calculated by means of CALPHAD (CALculation of PHAse Diagrams) approach, which is a useful tool to develop and design novel materials [5–7]. A Python-based program is developed to perform the automatic calculation and results extraction, as is indispensable for the high-throughput thermodynamic calculation technique, which is also a constituent of the Ocean of Data infrastructure [8]. In addition, key criteria are combined to filter the appropriate compositions of industrial 6016 Al-alloys, providing a valuable guidance to the experimentalists and reducing unnecessary trial-and-error tests. 2. Model description The standard composition of 6016 alloy [9] as well as the selected compositions for calculations are listed in Table 1. Since Mg and Si are the major components to form strengthening precipitates, the composition step for Mg and Si is defined as 0.05 wt%, which is sufficient low to meet the manufacture accuracy of industrial aluminum alloys. The Cu is another important additive, however it is not emphasized in the standard 6016 alloy grade, in the present work the composition of Cu is
First author and Corresponding author at: 30 Xueyuan Road, Haidian District, Beijing 100083, PR China. E-mail address: [email protected] (C. Zhang).
https://doi.org/10.1016/j.commatsci.2019.05.022 Received 5 April 2019; Received in revised form 11 May 2019; Accepted 12 May 2019 Available online 18 May 2019 0927-0256/ © 2019 Elsevier B.V. All rights reserved.
Computational Materials Science 167 (2019) 19–24
C. Zhang, et al.
Table 1 The standard composition of 6016 alloy and the selected compositions for calculations. Component
Al
Si
Mg
Fe
Cu
Mn
Zn
Cr
Ti
Others
Standard, wt% Selected, wt%
Bal. Bal.
1.00–1.50 Δ = 0.05a
0.25–0.60 Δ = 0.05
≤0.50 0.40
≤ 0.20 0.10, 0.20
≤0.20 0.10
≤0.20 0.15
≤0.10 0.05
≤0.15 0.10
≤0.15 0.00
a
Δ = 0.05 is the composition step within the standard composition interval.
selected to be 0.1 and 0.2 wt% for calculations. The other components are impurities such as Fe, Mn, Zn, Cr, Ti, and their composition are placed in moderate values. Based on the above selections, a total number of 176 sets of alloy composition are created. To obtain the primary phase fraction, precipitates fraction and phase composition of 6016 alloy, the thermodynamic calculation based on the Scheil-Gulliver model [10,11] was performed via Thermo-Calc software [12] employing the COST507 thermodynamic database [13]. Since the 6016 is a classic low-concentrated Al-alloy grade and mainly based on Al-Mg-Si system, the COST507 database was validated to be sufficient to calculate the phase equilibria reasonably [14–16]. In the Scheil-Gulliver model, several assumptions were proposed, such as no diffusion in the solids and complete diffusion in the liquid. The calculated Scheil-Gulliver solidification diagram and solidified phase fraction for 6016 alloy are shown in Fig. 1(a)–(b), with the composition of Al-1.1Si-0.5Mg-0.4Fe-0.2Cu-0.1Mn-0.15Zn-0.05Cr-0.1Ti as a case, in which the phase equilibria information can be obtained accordingly. The directional solidification and semi-continuous casting are normally performed for industrial aluminum alloys, the initial transient, steady-state and final transient solidification layers are existed in the casting ingot [17]. The initial transient is a composition macro-segregation layer, while the steady-state layer is homogeneous and beneficial to the subsequent processes. The establishment of a steady-state boundary layer requires a distance of growth which corresponds to the length of the initial transient, and can be described by the equation below [17]:
C k ·h·V ⎞ ⎤ CL* = ⎛ 0 ⎞ ⎡1 − (1 − k ) exp ⎛− ⎢ k D ⎠⎥ ⎝ ⎠⎣ ⎝ ⎦
(1)
CS* = k ·CL*
(2)
Fig. 2. General infrastructure of the high-throughput calculation program AEET. Using thermodynamic parameters from given database, and the output data together with other criteria are used to screen the target compositions.
In the present work, the amount of α-AlFeSi and β-AlFeSi harmful phases as well as the length of initial transient layer are the key factors to screen the reasonable composition sets of 6016 aluminum alloys. 3. HTC infrastructure
in which CL* is the liquid composition at solid/liquid interface, CS* is the solid composition at the solid/liquid interface, C0 is the alloy nominal composition, k is the equilibrium distribution coefficient, h is the solidified length, V is the crystallization velocity (m/s) and D is the diffusion coefficient in liquid phase (m2/s). Eq. (1) permits an estimation of the length that must solidify before the steady state is reached.
The calculation of single Scheil-Gulliver solidification task can be performed by commands typing or TCM file execution for users of Thermo-Calc software. However, it is low efficient and time-consuming to accomplish the calculations of 176 composition sets of 6016 aluminum alloys, therefore the high-throughput calculation (HTC) method is developed. The HTC infrastructure is schematically shown in Fig. 2, it
Fig. 1. The calculated (a) Scheil-Gulliver solidification diagram and (b) solidified phase fraction for 6016 alloy with the composition of Al-1.1Si-0.5 Mg-0.4Fe-0.2Cu0.1Mn-0.15Zn-0.05Cr-0.1Ti. 20
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Fig. 3. The Python coding for the (a) generation of TCM files and (b) results file extraction, which are integrated into AEET program.
the next section.
consists of Input layer, Execution layer and Output layer. For the Input layer, the alloy composition range and composition step are defined, while the thermodynamic database should be placed here. For the Execution layer, the Generate TCM file, Execute ThermoCalc and Extract results file are the three major steps. In the step of ‘Generate TCM file’, the calculation commands files are generated based on the selected 176 composition sets of 6016 aluminum alloys. These files are Thermo-Calc macro (TCM) format that contain alloy composition, initial temperature, stable phases and output items. The generation of TCM files can be performed by a Python coding shown in Fig. 3(a), by which the 176 TCM files corresponding to the 176 composition sets are generated. To speed up the running efficiency of Thermo-Calc software, the 176 files are integrated into one file. In the step of ‘Execute Thermo-Calc’, the Thermo-Calc was switched on when detected the appearance of TCM file, and will execute the commands within it. After which, the 176 sets of results files for the primary phase fraction, precipitates fraction and phase composition of 6016 alloy can be obtained. In the step of ‘Extract results file’, the calculated results are extracted from the output files (EXP files). Since the primary phase fraction, precipitates fraction and phase composition data are stored with logical rules defined by Thermo-Calc, the required data can be collected by following it. The Python coding for this function is displayed in Fig. 3(b). For the Output layer, all the extracted data are stored in Excel-type file, which is convenient for the subsequent analytical processing. To realize the above HTC tasks, a Python-based program called Automatic Execution and Extraction Tasks (AEET) is developed, which integrated the Input layer, Execution layer and Output layer. The AEET can generate the commands of calculations, execute the Thermo-Calc software and then extract the key data of output files automatically. In the present work, the amount of precipitates, especially α-AlFeSi and βAlFeSi harmful phases, together with other key criteria are used to screen the appropriate alloy compositions, which will be discussed in
4. Results and discussion The massive phase equilibria data of 6016 aluminum alloy are obtained via HTC approach; it can be used to analyze the influence of alloy composition on the primary phase fraction, solidified phase fraction as well as phase composition. In addition, the target compositions can be screened based on the above phase equilibria information, which is the subsequent work after AEET, as shown in Fig. 2. 4.1. High-throughput calculation results The fraction of primary phase (Al), strengthening phase Mg2Si and harmful phases α-AlFeSi, β-AlFeSi after solidification of 6016 Al-alloys are the major concerns on industrial application. In the present work, the Cu content has values of 0.1 and 0.2 wt%, since it do not change the trend of phase fraction evolution during solidification, it is representative to show the results with Cu content of 0.1 wt%. To facilitate reading, these HTC results are presented in contour map forms and shown in Fig. 4(a)–(d). As can be seen from Fig. 4(a), the fraction of primary phase (Al) is enriched in the low-Mg and low-Si place. This is due to the (Al) is mainly consisted of Al with minor amount of Mg, Si and other elements. The higher concentrated solute atoms is able to prompt the formation of precipitates such as Mg2Si and AlFeSi phases, and thus lowered the primary phase fraction. Besides, it also makes the high-Mg and high-Si region has larger amount of Mg2Si phase, as shown in Fig. 4(b). The composition of α-AlFeSi and β-AlFeSi phases are close to the stoichiometry of Al12Fe3Si2 and Al5FeSi, respectively [18–20]. Therefore, the β-AlFeSi phase has higher Si mole fraction than α-AlFeSi, and enriched in the high-Si corner, whereas α-AlFeSi phase enriched in the low-Si corner, which is in accordance with Fig. 4(c)–(d). It could be noted that the Mg content has effect on the formation of α-AlFeSi and β21
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Fig. 4. The calculated fraction of (a) primary phase (Al), (b) strengthening phase Mg2Si, (c) harmful phase α-AlFeSi and (d) β-AlFeSi after the solidification of 6016 Al-alloys.
Fig. 5. The calculated (a) phase equilibria of 6016 alloy at 300 °C in the Al-rich corner and (b) the solubility limits of Mg + Si in matrix (Al) phase. The calculations are performed by Thermo-Calc software using COST507 database.
AlFeSi phases, the increase of Mg leads to the increase of α-AlFeSi phase and decrease of β-AlFeSi phase.
target compositions. In the first cycle, the solubility limits of solute atoms in (Al) phase is considered. The phase equilibria of 6016 alloy at 300 °C and in the Alrich corner are calculated by Thermo-Calc software using COST507 database, as displayed in Fig. 5(a). The (Al) phase is coexisted with αAlFeSi and β-AlFeSi phases, which is due to the high Fe content (0.4 wt %) used in this calculation. Since the strengthening phase Mg2Si should be dissolved into the matrix phase before artificial aging for 6xxx series aluminum alloys, the solubility limits of Mg + Si in (Al) phase is
4.2. Alloy composition screening The appropriate 6016 alloy compositions can be reversely designed based on the phase equilibria information and other criteria, including solubility limits, Mg/Si/Cu amount, α/β-AlFeSi phase fraction and initial transient length. Hence, four cycles are involved to screen the 22
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Fig. 6. The composition screening of 6016 alloys based on the criteria of (a) solubility limits, (b) Mg/Si/Cu amount, (c) α/β-AlFeSi phase fraction and (d) initial transient length. The sphere symbols indicate the data are included in the next cycle, while the cube symbols are excluded.
Table 2 The selected 20 appropriate compositions for the development of industrial 6016 alloy.
Fig. 7. The calculated equilibrium distribution coefficients for Mg and Si of 6016 Al-alloys.
calculated and shown in Fig. 5(b). As can be seen, the matrix (Al) phase can dissolve more solute atoms at higher temperatures until the eutectic reaction take place, which is Liquid→(Al) + Mg2Si+(Si) at 557 °C. Based on the present calculations, the solubility limits of (Al) at 557, 500, 400 and 300 °C are 1.97, 1.19, 0.41 and 0.10 wt%, respectively. The above calculated results are in good agreement with the
No.
Al
Si
Mg
Fe
Cu
Mn
Zn
Cr
Ti
Others
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal. Bal.
1.20 1.20 1.25 1.25 1.25 1.25 1.25 1.30 1.30 1.30 1.35 1.20 1.25 1.25 1.25 1.25 1.25 1.30 1.30 1.35
0.45 0.50 0.40 0.45 0.50 0.55 0.60 0.50 0.55 0.60 0.55 0.50 0.40 0.45 0.50 0.55 0.60 0.55 0.60 0.55
0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40
0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20
0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15
0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05
0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
experimental determinations by Ji et al. [21], indicating the reasonable phase equilibria are obtained by thermodynamic calculations. Considering the effect of impurities, the solubility limits cannot reach the 23
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aluminum alloys, but has potentials to many kinds of high-throughput thermodynamic and kinetic calculations.
ideal maximum value of 1.97 wt%, therefore the criteria is set to be 1.90 wt%, and the Mg + Si amount higher than this value are excluded from the composition sets, as depicted in Fig. 6(a). In the second cycle, the solid solution strengthening and dispersion strengthening effects contributed from the solute atoms and precipitates are considered. The strengthening phase of 6016 aluminum alloy is mainly needle-shaped β′′. The excess of Si can promote the precipitation of β′′ with uniform distribution, and the microstructure is refined to achieve higher strength [22]. Therefore the automotive body panel aluminum alloy currently used in industry usually contains excess Si. For the strengthening phase β′′, the Si/Mg mass ratio is 0.92, and the screening standard is set to be w(Si)/w(Mg) > 0.92. The solid solution strengthening is mainly from the solute atoms of Mg, Si and Cu. Based on the experimental work by Simões et al. [23], for w(Mg) + w(Si) + w (Cu) larger than 1.50 wt% the aluminum alloy exhibits better strength property, and the w(Mg) + w(Si) + w(Cu) > 1.50 wt% is another selected criteria, as shown in Fig. 6(b). In the third cycle, the fraction of precipitated α-AlFeSi and β-AlFeSi phases are taken into consideration. During the homogenization of 6xxx series aluminum alloy, the α-AlFeSi and β-AlFeSi phases are brittle and act as fracture sources. Due to the complex crystal structure of the AlFeSi phases, the atomic diffusion rate is slow, and it takes longer time to dissolve into the matrix during the homogenization process [24]. If the AlFeSi phases are not fully dissolved, it is unfavorable for the subsequent rolling process. The fraction of α-AlFeSi and β-AlFeSi phases is displayed in Fig. 6(c), and the composition sets for the lower half of AlFeSi phase fraction are selected and transferred to the next cycle. In the last cycle of the present work, the length of initial transient macro-segregation layer is evaluated. To calculate the length of initial transient layer, the Eq. (1) described in Section 2 is utilized. Since the crystallization velocity V is a fixed processing parameter during manufacture and the diffusion coefficient D has minor variation within the 6016 standard composition ranges, these two values can be reduced. Then the length is correlated with equilibrium distribution coefficient k and nominal composition. The calculated characteristic distance required to establish a steady-state planar interface having lower limit of the standard composition [17] is shown in Fig. 6(d). As the length of segregation for Si is larger than that of Mg, only the results for Si is displayed in the figure. The distribution coefficient k used in the above calculation is obtained by thermodynamic calculations as shown in Fig. 7, in which the Thermo-Calc software and COST507 database are applied. Based on all the above calculations, the final 20 compositions are selected and listed in Table 2, which are promising composition sets to be attempted of developing 6016 industrial alloys. The reliable and applicable of COST507 database ensured the calculated phase equilibria can be used for composition screening and materials selection of 6016 alloys.
CRediT authorship contribution statement Cong Zhang: Formal analysis, Validation, Visualization, Writing original draft, Writing - review & editing. Xue Jiang: Conceptualization, Methodology. Ruijie Zhang: Investigation. Xin Wang: Data curation, Software. Haiqing Yin: Methodology, Project administration. Xuanhui Qu: Project administration. Zi-Kui Liu: Validation. Acknowledgements The financial supports from the National Key Research and Development Program of China (Grant No. 2016YFB0700503), National Natural Science Foundation of China (Grant No. 51701013) and Fundamental Research Funds for the Central Universities (Grant No. FRF-TP-17-005A1) are greatly acknowledged. References [1] W.S. Miller, L. Zhuang, J. Bottema, A.J. Wittebrood, P. De Smet, A. Haszler, A. Vieregge, Recent development in aluminum alloys for the automotive industry, Mater. Sci. Eng. A 280 (2000) 37–49. [2] Y. Birol, Pre-aging to improve bake hardening in a twin-roll cast Al-Mg-Si alloy, Mater. Sci. Eng. A 391 (2005) 175–180. [3] W.J. Liang, P.A. Rometsch, L.F. Cao, N. Birbilis, General aspects related to the corrosion of 6xxx series aluminium alloys: exploring the influence of Mg/Si ratio and Cu, Corros. Sci. 76 (2013) 119–128. [4] C.D. Marioara, S.J. Andersen, H.W. Zandbergen, R. Holmestad, The influence of alloy composition on precipitates of the Al-Mg-Si system, Metall. Mater. Trans. A 36 (2005) 691–702. [5] N. Li, W. Zhang, Y. Du, W. Xie, G. Wen, S. Wang, A new approach to control the segregation of (Ta,W)C cubic phase in ultrafine WC-10Co-0.5Ta cemented carbides, Scr. Mater. 100 (2015) 48–50. [6] C. Zhang, H. Yin, R. Zhang, X. Jiang, G. Liu, Y. Du, Experimental and thermodynamic investigation of gradient zone formation for Ti(C,N)-based cermets sintered in nitrogen atmosphere, Ceram. Int. 43 (2017) 12089–12094. [7] R. Jha, N. Chakraborti, D.R. Diercks, A.P. Stebner, C.V. Ciobanu, Combined machine learning and CALPHAD approach for discovering processing-structure relationships in soft magnetic alloys, Comput. Mater. Sci. 150 (2018) 202–211. [8] Z.-K. Liu, Ocean of data: integrating first-principles calculations and CALPHAD modeling with machine learning, J. Phase Equilib. Diffus. 39 (2018) 635–649. [9] International Alloy Designations and Chemical Composition Limits for Wrought Aluminum and Wrought Aluminum Alloys, The Aluminum Association, Arlington, VA, 2009. [10] G.H. Gulliver, The quantitative effect of rapid cooling upon the constitution of binary alloys, J. Inst. Met. 9 (1913) 120–157. [11] E. Scheil, Unbroken series of solid solutions in the binary systems of the elements, Z. Metallkd. 34 (1942) 242–246. [12] B. Sundman, B. Jansson, J.O. Andersson, The thermo-calc databank system, Calphad 9 (1985) 153–190. [13] I. Ansara, COST 507-Thermochemical Database for Light Metal Alloys, European Commission, Brussels/Luxembourg, 1995. [14] E. Balitchev, T. Jantzen, I. Hurtado, D. Neuschutz, Thermodynamic assessment of the quaternary system Al-Fe-Mn-Si in the Al-rich corner, Calphad 27 (2003) 275–278. [15] Y. Tang, Y. Du, L. Zhang, X. Yuan, G. Kaptay, Thermodynamic description of the Al-Mg-Si system using a new formulation for the temperature dependence of the excess Gibbs energy, Thermochim. Acta 527 (2012) 131–142. [16] H.-L. Chen, Q. Chen, A. Engstroem, Development and applications of the TCAL aluminum alloy database, Calphad 62 (2018) 154–171. [17] W. Kurz, D.J. Fisher, Fundamental of Solidification, Trans Tech Publications Ltd, Switzerland, 1998 Fourth Revised Edition. [18] J.M. Yu, N. Wanderka, G. Miehe, J. Banhart, Intermetallic phases in high purity Al-10Si0.3Fe cast alloys with and without Sr modification studied by FIB tomography and TEM, Intermetallics 72 (2016) 53–61. [19] N. Bayat, T. Carlberg, M. Cieslar, In-situ study of phase transformations during homogenization of 6005 and 6082 Al alloys, J. Alloy. Compd. 725 (2017) 504–509. [20] R. Kumar, A. Gupta, A. Kumar, R.N. Chouhan, R.K. Khatirkar, Microstructure and texture development during deformation and recrystallization in strip cast AA8011 aluminum alloy, J. Alloy. Compd. 742 (2018) 369–382. [21] Y. Ji, H. Zhong, P. Hu, F. Guo, Use of thermodynamic calculation to predict the effect of Si on the ageing behavior of Al-Mg-Si-Cu alloys, Mater. Des. 32 (2011) 2974–2977. [22] A.K. Gupta, D.J. Lloyd, S.A. Court, Precipitation hardening in Al-Mg-Si alloys with and without excess Si, Mater. Sci. Eng. A 316 (2001) 11–17. [23] V. Simões, H. Laurent, M. Oliveira, L. Menezes, The influence of warm forming in natural aging and springback of Al-Mg-Si alloys, Int. J. Mater. Form. 12 (2019) 57–68. [24] G. Sha, K. O'Reilly, B. Cantor, Characterization of Fe-Rich intermetallic phases in a 6xxx series Al alloy, Mater. Sci. Forum. 519–521 (2006) 1721–1726.
5. Conclusion The appropriate 6016 alloy compositions are reversely designed based on the high-throughput calculation (HTC) method and ScheilGulliver model, the primary phase fraction, precipitates fraction and phase composition are calculated for the entire composition ranges of standard 6016 Al-alloy. A Python-based program AEET (Automatic Execution and Extraction Tasks) is developed to improve the calculation efficiency, which can generate the commands of calculations, execute the Thermo-Calc software and then extract the key data of output files automatically. The fraction of harmful α/β-AlFeSi phases together with other criteria, such as solubility limits, Mg/Si/Cu amount and initial transient length, are used to filter the appropriate compositions of industrial 6016 Al-alloys. This method provides a valuable guidance to the experimentalists, for the time-consuming and unnecessary trial-and-error tests can be reduced. Moreover, the present HTC approach is not limited to the solidification calculations for 6016 24