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Abstract

In contrast to casting to conventional non-reusable “sand” moulds, for which calculating technique for an optimum design of the gating system is comparatively well-developed, a trial-and-error method is applied mostly for casting to ceramic shell moulds made by the investment casting technology. A technologist selects from gating systems of several types (that are standardized by the foundry mostly) on the basis of experience. However, this approach is not sustainable with ever growing demands on quality of castings and also the economy of their fabrication as well as with new types of complex sizeable castings introduced to the production gradually (by new customers from the aircraft industry above all) any more. The simulation software may be used as a possible tool for making the process of optimising gating systems more effective.

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Authors and Affiliations

I. Stachovec
M. Horáček
L. Zemčík
V. Kolda
J. Horenský
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Abstract

The purpose of this paper was to develop a methodology for diagnosing the causes of die-casting defects based on advanced modelling, to correctly diagnose and identify process parameters that have a significant impact on product defect generation, optimize the process parameters and rise the products’ quality, thereby improving the manufacturing process efficiency. The industrial data used for modelling came from foundry being a leading manufacturer of the high-pressure die-casting production process of aluminum cylinder blocks for the world's leading automotive brands. The paper presents some aspects related to data analytics in the era of Industry 4.0. and Smart Factory concepts. The methodology includes computation tools for advanced data analysis and modelling, such as ANOVA (analysis of variance), ANN (artificial neural networks) both applied on the Statistica platform, then gradient and evolutionary optimization methods applied in MS Excel program’s Solver add-in. The main features of the presented methodology are explained and presented in tables and illustrated with appropriate graphs. All opportunities and risks of implementing data-driven modelling systems in high-pressure die-casting processes have been considered.
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Bibliography

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[2] Campbell, J. (2003). Castings, the new metallurgy of cast materials, second edition. Elsevier Science Ltd., ISBN: 9780750647908, 307-312.
[3] Kochański, A.W. & Perzyk, M. (2002). Identification of causes of porosity defects in steel castings with the use of artificial neural networks. Archives of Foundry. 2(5), 87-92. ISSN 1642-5308.
[4] Falęcki, Z. (1997). Analysis of casting defects. Kraków: AGH Publishers.
[5] Kim, J., Kim, J., Lee, J. (2020). Die-Casting defect prediction and diagnosis system using process condition data. Procedia Manufacturing. 51, 359-364. DOI: 10.1016/j.promfg.2020.10.051.
[6] Lewis, M. (2018). Seeing through the Cloud of Industry 4.0. In 73rd WFC, 23-27, (pp. 519-520). Krakow, Poland: Polish Foundrymen’s Association.
[7] Perzyk, M., Dybowski, B. & Kozłowski, J. (2019). Introducing advanced data analytics in perspective of industry 4.0. in die casting foundry. Archives of Foundry Engineering. 19(1), 53-57.
[8] Perzyk, M., Kozłowski, J. & Wisłocki, M., (2013). Advanced methods of foundry processes control. Archives of Metallurgy and Materials. 58(3), 899-902. DOI: 10.2478/amm-2013-0096
[9] Makhlouf, M.M., Apelian, D. & Wang, L. (1998). Microstructures and properties of aluminum die casting alloys. North American Die Casting. https://doi.org/10.2172/751030
[10] Tariq, S., Tariq, A., Masud, M. & Rehman, Z. (2021). Minimizing the casting defects in high pressure die casting using taguchi analysis. Scientia Iranica. DOI: 10.24200/sci.2021.56545.4779.
[11] Fracchia, E., Lombardo, S., & Rosso, M. (2018). Case study of a functionally graded aluminum part. Applied Sciences. 8(7), 1113.
[12] Dargusch, M.S., Dour, G., Schauer, N., Dinnis, C.M. & Savage, G. (2006). The influence of pressure during solidification of high pressure die cast aluminium telecommunications components. Journal of Materials Processing Technology. 180(1-3), 37-43.
[13] Bonollo, F., Gramegna, N., Timelli, G. High pressure die-casting: contradictions and challenges. JOM: the journal of the Minerals, Metals & Materials Society. 67(5), 901-908. DOI: 10.1007/s11837-015-1333-8.
[14] Adamane, A.R., Arnberg, L., Fiorese, E., Timelli, G., Bonollo, F. (2015). Influence of injection parameters on the porosity and tensile properties of high-pressure die cast Al-Si alloys: A Review. International Journal of Meterials. 9(1), 43-53.
[15] Gramegna, N. & Bonollo, F. (2016). HPDC foundry competitiveness based on smart Control and Cognitive system in Al-alloy products. La Metallurgia Italiana. 6, 21-24.
[16] Łuszczak, M. & Dańko, R. (2013). State the issues in the casting of large structural castings in aluminium alloys. Archives of Foundry Engineering. 13(3), 113-116. ISSN (1897-3310).
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[23] https://natemat.pl/blogi/ryszardtadeusiewicz/129195,pierwszy-dzialajacy-techniczny-model-mozgu

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Authors and Affiliations

A. Okuniewska
1
M.A. Perzyk
1
J. Kozłowski
1

  1. Institute of Manufacturing Technologies, Warsaw University of Technology, Narbutta 85, 02-524 Warsaw, Poland
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Abstract

The article concerns the experimental verification of the numerical model simulating the solidification and cooling processes proceeding in the domain of cast iron casting. The approximate course of the function describing the evolution of latent heat and the value of substitute specific heat resulting from its course were obtained using the thermal and derivative analysis (TDA) method The TDA was also used to measure the cooling curves at the distinguished points of the casting. The results obtained in this way were compared with the calculated cooling curves at the same points. At the stage of numerical computations, the explicit scheme of the finite difference method was applied. The agreement between the measured and calculated cooling curves is fully satisfactory.
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Bibliography

[1] Mendakiewicz, J. (2011). Identification of the solidification parameters of casting alloys on the example of grey cast iron. Monografia. Gliwice: Wyd. Pol. Śl. (in Polish).
[2] Jiji, L.M. (2009). Heat conduction. Third Edition. Springer.
[3] Mochnacki, B. & Majchrzak, E. (2007). Identification of macro and micro parameters in solidification model. Bulletin of the Polish Academy of Sciences. Technical Sciences. 55(1), 107-113.
[4] Kapturkiewicz, W. (2003). Modelling of cast iron solidification. Cracow: Akapit.
[5] Majchrzak, E., Mendakiewicz, J. & Piasecka-Belkhayat, A. (2005). Algorithm of mould thermal parameters identification in the system casting–mould–environment. Journal of Materials Processing Technology. 162-163, 1544-1549.
[6] Mochnacki, B., Suchy, J.S. (1995). Numerical methods in computations of foundry processes. Cracow: PFTA.
[7] Ciesielski, M. & Mochnacki, B. (2019). Comparison of approaches to the numerical modelling of pure metals solidification using the control volume method. International Journal of Cast Metals Research. 32(4), 213-220. https://doi.org/10.1080/13640461.2019.1607650
[8] Majchrzak, E., Mochnacki, B., Suchy, J.S (2008). Identification of substitute thermal capacity of solidifying alloy. Journal of Theoretical and Applied Mechanics. 46(2), 257-268.

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Authors and Affiliations

J. Mendakiewicz
1
ORCID: ORCID

  1. Department of Computational Mechanics and Engineering, Silesian University of Technology, Konarskiego18A, 44-100 Gliwice, Poland
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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.
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[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
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[9] Hillert, M. (2008). Phase Equilibria, Phase Diagrams and Phase Transformations. Cambridge: Cambridge University Press.
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[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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Abstract

The paper includes validation studies of the flow module of the NovaFlow&Solid simulation code. Experiments of ductile iron and gray iron casting in a spiral test of castability were carried out. Casting experiments were then carried out in industrial conditions in the Ferrex Foundry in Poznań and the results are the castability spiral length and local cast iron rate during mould cavity pouring. Simulation tests using NovaFlow&Solid Control Volume code were made. The technological castability test was used to determine thermal-physical data through simplified inversion problem. Influence of physical parameters in the database of simulation code on the spiral length obtained as the result of simulation was analyzed. It was found that critical fraction of capillary flow CLFdown has the biggest impact on cast iron castability in the simulation code. The simulations resulted in defining parameters of gray iron GJL 250 and ductile iron GJS-400-15. For the parameters set, the length of castability spiral in simulations was in accordance with casting experiments.

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Authors and Affiliations

P. Popielarski
ORCID: ORCID
J. Hajkowski
R. Sika
Z. Ignaszak
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Abstract

During the casting cycle, the relatively cold material of the mold comes into contact with the significantly higher temperature melt, which causes high temperature fluctuations on the face of the mold and in its volume, which cause cyclic temperature stress. The submitted article is based on conclusions of the article “Evaluation of the temperature distribution of a die casting mold of X38CrMoV5_1 steel”, in which the modification of temperature relations of the mold in the direction from the mold face to its volume was investigated. In current article, the influence of the tempering channel distance on the temperature modification in the volume of high pressure die casting mold is investigated. Three variants of the tempering channels placements with different location respecting the mold cavity were investigated. The temperature was monitored in two selected locations, with distribution of 1mm, 2mm, 5mm, 10mm and 20mm in the direction from the mold cavity surface to the volume of fixed and movable part of the mold. As a comparative parameter, the temperature of the melt in the center of the runner above the measuring point and the temperature of the melt close to the face of the mold were monitored. The measurement was performed using Magmasoft simulation software. It was discovered that up to a distance of 5mm from the face of the mold, a zone with complete heat transit without its accumulation occurs. Above this limit, the mold begins to accumulate heat, and from distance of 20mm from the face of the mold, the heat gradually passes into the entire mass of the mold without significant temperature fluctuations. The propositions derived from the results of the experiments presented at the end of the article will subsequently be experimentally verified in further research works.
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Bibliography

[1] Ebrahimi, A., Fritsching, U., Heuser, M., Lehmhus, D., Struß, A., Toenjes, A., von Hehl, A. (2020). A digital twin approach to predict and compensate distortion in a High Pressure Die Casting (HPDC) process chain. In Proceedings of the 5th International Conference on System-Integrated Intelligence, 11-13 November 2020 (pp. 144-149). Bremen: Elsevier B.V. DOI: 10.1016/j.promfg.2020.11.026.
[2] Bi, C., Gou, Z. & Xiong, S. (2015). Modeling and simulation for die casting mould filling process using cartesian cut cell approach. International Journal of Cast Metals Research. 28(4), 234-241. DOI: 10.1179/1743133615Y.0000000006.
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[6] Jiao, X., Liu, C., Wang, J., Guo, Z., Wang, J., Wang, Z., Guo, J. & Xiong, S. (2020). On the characterization of microstructure and fracture in a high-pressure die-casting Al-10 wt%Si alloy. Progress in Natural Science: Materials International. 30(2), 221-228. DOI: 10.1016/j.pnsc.2019.04.008.
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[8] Majernik, J. & Podaril. M. (2019). Evaluation of the temperature distribution of a die casting mold of X38CrMoV5_1 steel. Archives of Foundry Engineering. 19(2), 107-112. DOI: 10.24425/afe.2019.127125.
[9] Ružbarský, J., Paško, J., & Gašpár, Š. (2014). Technigques of Die Casting. Lüdenscheid: RAM-Verlag.
[10] Trytek, A. Orłowicz, A.W., Tupaj, M., Mróz, M., Markowska, O., Bąk, G. & Abram, T. (2016) The effect of a thin-wall casting mould cavity filling conditions on the casting surface quality. Archives of Foundry Engineering. 16(4), 222-226. DOI: 10.1515/afe-2016-0113.
[11] Gašpár, Š., Paško, J., & Majerník, J. (2017). Infuence of Structure Adjustment of Gating System of Casting Mould upon the Quality of Die Cast. Lüdenscheid: RAM-Verlag.
[12] Noga, P., Tuz, L., Żaba, K. & Zwoliński, A. (2021). Analysis of microstructure and mechanical properties of alsi11 after chip recycling, co-extrusion, and arc welding. Materials. 14(11), 3124, 1-22. DOI: 10.3390/ma14113124.
[13] Majernik, J. Gaspar, S., Podaril, M. & Coranic, T. (2020). Evaluation of thermal conditions at cast-die casting mold interface. MM Science Journal. 2020(November), 4112-4118. DOI: 10.17973/MMSJ.2020_11_2020041.
[14] Karková, M., Majerník, J. & Kmec, J. (2017). Analysis of influencing the macrostrukture and hardness of casting surface layer by changing conditions of crystallization. MM Science Journal. 1910-1913. DOI: 10.17973/MMSJ.2017_12_201720.
[15] Gašpár, Š., Pasko, J., Malik, J., Panda, A., Jurko, J. & Maseenik, J. (2012). Dependence of pressure die casting quality on die casting plunger velocity inside a filling chamber of a pressure die casting machine. Advanced Science Letters. 14(1), 499-502. DOI: 10.1166/asl.2012.3989.
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Authors and Affiliations

J. Majerník
1
ORCID: ORCID
M. Podaril
1
ORCID: ORCID
M. Majernikova
1

  1. Institute of Technology and Business in České Budějovice, Czech Republic
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Abstract

Computational Materials Engineering (CME) is a high technological approach used to design and develop new materials including the physical, thermal and mechanical properties by combining materials models at multiple techniques. With the recent advances in technology, the importance of microstructural design in CME environments and the contribution that such an approach can make in the estimation of material properties in simulations are frequently discussed in scientific, academic, and industrial platforms. Determination of the raw material characteristics that can be modeled in a virtual environment at an atomic scale by means of simulation programs plays a big role in combining experimental and virtual worlds and creating digital twins of the production chain and the products. In this study, a new generation, alternative and effective approach that could be used to the development of Al-Si based wheel casting alloys is proposed. This approach is based on the procedure of optimizing the physical and thermodynamic alloy properties developed in a computer environment with the CME technique before the casting phase. This article demonstrates the applicability of this approach in alloy development studies to produce Al-Si alloy wheels using the low pressure die casting (LPDC) method. With this study, an alternative and economical way is presented to the alloy development studies by trial and error in the aluminum casting industry. In other respects, since the study is directly related to the automotive industry, the reduction in fuel consumption in vehicles is an expected effect, as the new alloy aims to reduce the weight of the wheels. In addition to conserving energy, reducing carbon emissions also highlights the environmental aspects of this study.
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Bibliography

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Authors and Affiliations

T. Yağcı
1
Ü. Cöcen
1
O. Çulha
2

  1. Dokuz Eylul University, Dept. of Metallurgical and Materials Engineering, İzmir, Turkey
  2. Manisa Celal Bayar University, Dept. of Metallurgical and Materials Engineering, Manisa, Turkey

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