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Reconstruction of the Late Bronze Age Foundry Process in Greater Poland: Analyzes and Simulations. Case Study of Hoard from Przybysław

A. Garbacz-Klempka M. Piękoś M. Perek-Nowak J. Kozana P. Żak A. Fijołek P. Silska M. Stróżyk
Archives of Metallurgy and Materials | 2022 | vol. 67 | No 3 | 1125-1136 | DOI: 10.24425/amm.2022.139712
Keywords archaeometallurgy Copper alloys Casting CALPHAD Computer modeling Late Bronze Age
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Abstract

One of the most interesting categories of artifacts for archaeometallurgical research includes deposits of bronze items, so-called “metallurgists hoards”. They contain, aside of final products, many fragments of raw material and, moreover, metallurgical tools. An important source for the studies on the history of metallurgical technology is hoard from Przybysław, Greater Poland district.
Thus, the aim of the work is the identification and interpretation of bronze-working practices and strategies adopted by prehistoric communities of the Late Bronze Age and the Early Iron Age (ca. 600 BC). The examined objects are characterized in terms of their design, structure, and chemical composition. The methods chosen for the studies of artifacts include: metallographic macro- and microscopic observations using optical microscopy (OM) and scanning electron microscopy (SEM), the analysis of chemical composition with the methods of energy dispersive X-ray spectroscopy (EDS), and X-ray fluorescence (ED-XRF).
The thermodynamic analysis of the alloys was performed on the basis of the CALPHAD method. The experimental melts allowed to verify the theoretical considerations and to determine the characteristic temperatures of changes.
The old casting technology can be analyzed basing on computer modeling and computer simulation methods. Simulations in the MAGMASOFT® software are a good example to illustrate how to fill a mould cavity with a molten bronze for a hoop ornament. It is also an appropriate tool to determine temperature distribution in a mould. The simulations also show the possible disadvantages with this old technology.
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Authors and Affiliations

A. Garbacz-Klempka
1
ORCID: ORCID
M. Piękoś
1
ORCID: ORCID
M. Perek-Nowak
2
ORCID: ORCID
J. Kozana
1
ORCID: ORCID
P. Żak
1
ORCID: ORCID
A. Fijołek
1
ORCID: ORCID
P. Silska
3
ORCID: ORCID
M. Stróżyk
3
ORCID: ORCID
  1. AGH University of Science and Technology, Faculty of Foundry Engineering, Historical Layers Research Centre, Kraków, Poland
  2. AGH University of Science and Technology, Faculty of Non Ferrous Metals, Historical Layers Research Centre, Kraków, Poland
  3. Archaeological Museum in Poznań, Poznań, Poland

Thermodynamic Assessment of Mushy Zone in Directional Solidification

P. Mikołajczak L. Ratke
Archives of Foundry Engineering | 2015 | No 4 | DOI: 10.1515/afe-2015-0088
Keywords Aluminum alloys Directional solidification Mushy zone CALPHAD Fe intermetallics β-Al5FeSi
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Abstract

Solidification of AlSiFe alloys was studied using a directional solidification facility and the CALPHAD technique was applied to calculate

phase diagrams and to predict occurring phases. The specimens solidified by electromagnetic stirring showed segregation across, and the

measured chemical compositions were transferred into phase diagrams. The ternary phase diagrams presented different solidification paths

caused by segregation in each selected specimen. The property diagrams showed modification in the sequence and precipitation

temperature of the phases. It is proposed in the study to use thermodynamic calculations with Thermo-Calc which enables us to visualize

the mushy zone in directional solidification. 2D maps based on property diagrams show a mushy zone with a liquid channel in the

AlSi7Fe1.0 specimen center, where significant mass fraction (33%) of β-Al5FeSi phases may precipitate before α-Al dendrites form.

Otherwise liquid channel occurred almost empty of β in AlSi7Fe0.5 specimen and completely without β in AlSi9Fe0.2. The property

diagrams revealed also possible formation of α–Al8Fe2Si phases.

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Authors and Affiliations

P. Mikołajczak
L. Ratke

Impact of Values of Diffusion Coefficient on Results of Diffusion Modelling Driven by Chemical Potential Gradient

M. Wróbel A. Burbelko
Archives of Foundry Engineering | 2022 | vol. 22 | No 3 | 81-90 | DOI: 10.24425/afe.2022.140239
Keywords Application of information technology to the foundry industry Diffusion modelling Calphad Chemical potential Diffusion coefficient
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Abstract

In the paper critical role of including the right material parameters, as input values for computer modelling, is stressed. The presented model of diffusion, based on chemical potential gradient, in order to perform calculations, requires a parameter called mobility, which can be calculated using the diffusion coefficient. When analysing the diffusion problem, it is a common practice to assume the diffusion coefficient to be a constant within the range of temperature and chemical composition considered. By doing so the calculations are considerably simplified at the cost of the accuracy of the results. In order to make a reasoned decision, whether this simplification is desirable for particular systems and conditions, its impact on the accuracy of calculations needs to be assessed. The paper presents such evaluation by comparing results of modelling with a constant value of diffusion coefficient to results where the dependency of Di on temperature, chemical composition or both are added. The results show how a given deviation of diffusivity is correlated with the change in the final results. Simulations were performed in a single dimension for the FCC phase in Fe-C, Fe-Si and Fe-Mn systems. Different initial compositions and temperature profiles were used.
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Bibliography

[1] Lambers, J.V. & Sumner, A.C. (2016). Explorations in Numerical Analysis. World Scientific Publishing.
[2] Nishibata, T., Kohtake, T. & Kajihara, M. (2020). Kinetic analysis of uphill diffusion of carbon in austenite phase of low-carbon steels. Materials Transactions. 61(5), 909-918. DOI: 10.2320/matertrans.MT-M2019255.
[3] Wróbel, M., & Burbelko, A. (2022). A diffusion model of binary systems controlled by chemical potential gradient. Journal of Casting & Materials Engineering. 6(2), 39-44. DOI: 10.7494/jcme.2022.6.2.39.
[4] Porter, D.A., Easterling, K.E. & Sherif, M.Y. (2009). Phase transformations in metals and alloys. Boca Raton: CRC Press.
[5] Bhadeshia, H.K.D.H. (2021). Course MP6: Kinetics & Microstructure Modelling. University of Cambridge. Retrieved July 23 2021 from: https://www.phase-trans.msm.cam.ac.uk/teaching.html
[6] Bergethon, P.R. & Simons, E.R. (1990). Biophysical Chemistry: Molecules to Membranes. New York: Springer-Verlag. DOl: 10.1007/978-1-4612-3270-4
[7] Shewmon, P. (2016). Diffusion in Solids. Cham: Springer International Publishers
[8] Mehrer, H. (2007). Diffusion in Solids: Fundamentals, Methods, Materials, Diffusion-Controled Processes. Berlin – Heidelberg: Springer-Verlag
[9] Hillert, M. (2008). Phase Equilibria, Phase Diagrams and Phase Transformations. Cambridge: Cambridge University Press.
[10] Lukas, H.L., Fries, S.G. & Sundman, B. (2007). Computational Thermodynamics. Cambridge: Cambridge University Press.
[11] Brandes, E.A. & Brook, G.B. (Eds.) (1998). Smithells Metals Reference Book. 7th Edition. Oxford: Elsevier.
[12] Bergner, D., Khaddour, Y. & Lorx, S. (1989). Diffusion of Si in bcc- and fcc-Fe. Defect and Diffusion Forum. 66-69, 1407-1412. DOI: 10.4028/www.scientific.net/DDF.66-69.1407.
[13] Nohara, K. & Hirano, K. (1973). Self-diffusion and Interdiffusion in γ solid solutions of the iron-manganese system. Journal of the Japan Institute of Metals. 37(1), 51-61. https://doi.org/10.2320/jinstmet1952.37.1_51
[14] Gegner, J. (2006). Concentration- and temperature-dependent diffusion coefficient of carbon in FCC iron mathematically derived from literature data. In the 4th Int Conf Mathematical Modeling and Computer Simulation of Materials Technologies, Ariel, College of Judea and Samaria.
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Authors and Affiliations

M. Wróbel
1
ORCID: ORCID
A. Burbelko
1
ORCID: ORCID
  1. AGH University of Science and Technology, Faculty of Foundry Engineering, al. A. Mickiewicza 30, 30-059 Krakow, Poland

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