=Paper=
{{Paper
|id=Vol-2035/paper1
|storemode=property
|title=Computational Modelling for Phase Transformation Prediction in Cast Aluminum-Iron-Silicon Alloys
|pdfUrl=https://ceur-ws.org/Vol-2035/paper1.pdf
|volume=Vol-2035
|authors=Alexander S. Zhilin,Valerija R. Yalunina,Vladimir V. Tokarev,Viktor A. Bykov,Jianguo Li,Joseph W. Newkirk,Tarak A. Amine
}}
==Computational Modelling for Phase Transformation Prediction in Cast Aluminum-Iron-Silicon Alloys==
https://ceur-ws.org/Vol-2035/paper1.pdf
Proceedings of Information Technologies, Telecommunications and Control Systems (ITTCS) - 2017
Computational Modelling for Phase Transformation Prediction in Cast
Aluminum-Iron-Silicon Alloys
Modern IT technologies for Materials Science applications
Alexander S. Zhilin, Valerija R. Viktor A. Bykov. Jianguo Li Newkirk J.W., Amine T.A.
Yalunina, Vladimir V. Tokarev Insitute of Metallurgy School of Materials Materials Science
Materials Science Department Ural Branch RAS, Tsinghua University Department
Ural Federal University Ural Federal Beijin, China Missouri Science and
Yekaterinburg, Russia University, Technology University
a.s.zhilin@urfu.ru Yekaterinburg, Rolla, USA
Russia jnewkirk@mst.edu
Abstract
The present work shows how it is possible to use computational modelling for
the prediction of phase transformation processes in cast aluminum-iron-
silicon alloys. Correlation between experimental and modelling data is also
discussed. Modelling of alloys phases doesn`t give the actual structure
components information, however it allows for prediction of phase
transformations during structure formation process under cooling.
1 Introduction
Aluminum-silicon alloys are materials which are widely used in large amounts in industrial fields [1]. Aluminum-silicon
alloys are casting alloys according to their manufacturing technology [2]. In the focus of the present work, these alloys are
the subject of investigation because the design of advanced properties material may be only made by taking into account
deep understanding of structural phase distribution and its influence on properties [3]. The present paper demonstrates how
modelling may help in searching among compositions of experimental alloys which will be evaluated for further work by
metallurgical methods. All modeled compositions contain iron. This addition is necessary to input for making the alloys
better suited for die casting technologies [4]. These technologies allow for the production of large final product volumes
daily at industrial facilities [5].
2 Experiment
The analyzed compositions are given in table 1. The selection of these compositions is based on the fact that iron improves
complete filling of the mold when the alloy is produced by casting technologies [6]. That is why it is necessary to know
what phases iron will form during the manufacturing process. The variation of silicon is also required for better
understanding of crystallization intervals for every composition. The models have been implemented using the computer
software “ThermoCalc 9.0” at Department of Materials Science in the Missouri University of Science and Technology,
Missouri State, USA.
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Proceedings of Information Technologies, Telecommunications and Control Systems (ITTCS) - 2017
Table 1. Analyzed compositions
Composition
Alloy
Al (%) Si (%) Fe (%)
Alloy 1 92% 6% 2%
Alloy 2 94% 4% 2%
Alloy 3 96% 2% 2%
Alloy 4 93,5% 6% 0,5%
Alloy 5 95,5% 4% 0,5%
Alloy 6 97,5% 2% 0,5%
3 Results and Discussion
Distribution curves of phases in terms of variation in the concentration of silicon were obtained by composition modeling
(fig. 1, 2). The regions of solid-solutions existence, secondary phases crystallization and liquid-solid equilibrium curves
can be defined on the resulting phase diagrams. The modelled results correspond to the theory [2-3]. Iron additions
influences the stability of iron-containing phases in “liquid + solid = solid” transformations as well as increasing the number
of these transformations. The obtained temperature boundaries of each phase show how the concentration of silicon
changes: it is reducing during cooling in all phases. Moderately narrow intervals of crystallization are observed in all alloys.
In future papers it will be discussed how reducing of silicon concentration in every phase influences on final structure
components distribution in experimental alloys which operate at room temperature. The major goal of the work is to find
how the final properties of experimental alloys correlate with predicted properties as well as what is the use of modern
modelling in understanding of real structure formation process.
Obviously, more silicon addition into the alloy results in differences in starting and finishing of crystallization, scale
of temperature crystallization intervals and positions of the “liquid + solid = solid” transformations. Of course, the presence
of these high temperature calculated phases in actual alloys was not proved by metallography; however, during
crystallization at low cooling rates the liquid phase might have zones with abnormal chemical composition which was
formed by a decrease in the content of alloying elements in the liquid phase situated among solid solution crystals. While
crystals of solid solution or chemical phase grow, the content of alloying elements in the liquid phase decreases
significantly. This leads to the formation of these zones with an unequal distribution of alloying elements in liquid phase.
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Proceedings of Information Technologies, Telecommunications and Control Systems (ITTCS) - 2017
a) b)
c)
Figure 1 – Calculated phase diagrams for alloys: a) 92%Al-6%Si-2%Fe; b) 94%Al-4%Si-2%Fe;c) 96%Al-2%Si-2%Fe
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Proceedings of Information Technologies, Telecommunications and Control Systems (ITTCS) - 2017
a) b)
c)
Figure 2 – Calculated phase diagrams for a) 93,5%Al-6%Si-0,5%Fe; b) 95,5%Al-4%Si-0,5%Fea) 97,5%Al-2%Si-
0,5%Fe
It is suggested that iron containing phases at high temperature are saturated by silicon. This results in diffusion of
silicon from iron containing phases during further cooling down to 250 °C.
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Proceedings of Information Technologies, Telecommunications and Control Systems (ITTCS) - 2017
Thus, modeling allows getting reasonable representations of the phase composition information in the high
temperature region and expected temperatures of phase transformation areas as well as values of changing of elements
percentage in phases during crystallization at low cooling rates. On the basis of simulated diagrams, it becomes possible to
choose alloy compositions and technologies for experimental alloys manufacturing which will be the next step of current
work in searching of advanced thermally conductive cast aluminum alloys compositions.
References
1. Tański, T., Labisz, K., Krupińska, B., Krupiński, M., Król, M., Maniara, R., Borek, W., “Analysis of crystallization
kinetics of cast aluminum-silicon alloy,” Journal of Thermal Analysis and Calorimetry, vol. 123 (1), pp. 63-74, 2016.
2. Kim, Y.-M., Kang, D.-S., Hong, S.-K., Kim, Y.-C., Kang, C.-S., Choi, S.-W, “Influence of variation in the silicon
content on the silicon precipitation in the Al–Si binary system,” Journal of Thermal Analysis and Calorimetry, vol.
128 (1), pp. 107-113, 2017.
3. Ye, H., “An overview of the development of Al-Si-alloy based material for engine applications,” Journal of Materials
Engineering and Performance, vol. 12 (3), pp. 288-297,2003.
4. Taylor, J.A., “Iron-containing intermetallic phases in Al-Si based casting alloys,” 11th International Congress On
Metallurgy & Materials Sam/Conamet 2011,vol. 1, pp. 19-33, 2012.
5. Jabłoński, M., Knych, T., Mamala, A., Smyrak, B., Wojtaszek, K., “Influence of Fe and Si addition on the properties
and structure conductivity aluminium,” Archives of Metallurgy and Materials, vol. 62 (3), pp. 1541-1547, 2017.
6. Mbuya, T.O., Odera, B.O., Ng'ang'a, S.P., “Influence of iron on castability and properties of aluminium silicon alloys:
literature review,” International Journal Of Cast Metals Research, vol. 16 (5), pp. 451-465, 2003.
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