Optimization of Side Feeders Systems by Means of Simulation of Solidification

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Simulation software can be used not only for checking the correctness of a particular design but also for finding rules which could be used in majority of future designs. In the present work the recommendations for optimal distance between a side feeder and a casting wall were formulated. The shrinkage problems with application of side feeders may arise from overheating of the moulding sand layer between casting wall and the feeder in case the neck is too short as well as formation of a hot spot at the junction of the neck and the casting. A large number of simulations using commercial software were carried out, in which the main independent variables were: the feeder’s neck length, type and geometry of the feeder, as well as geometry and material of the casting. It was found that the shrinkage defects do not appear for tubular castings, whereas for flat walled castings the neck length and the feeders’ geometry are important parameters to be set properly in order to avoid the shrinkage defects. The rules for optimal lengths were found using the Rough Sets Theory approach, separately for traditional and exothermic feeders.

[1] Perzyk, M. (2006). Data mining in foundry production. In K Świątkowski (Ed.), Research in Polish Metallurgy At the Beginning of XXI Century (pp. 255-276). Cracow: Committee of Metallurgy of the Polish Academy of Sciences.

[2] Wlodawer, R. (1966). Directional Solidification of Steel Castings. Oxford: Pergamon Press.

[3] Holzmüller, A. & Wlodawer, R. (1963). Zehn Jahre Speiser- Einguss-Verfahren fur Gusseisen. Giesserei. 50(25), 781-791.

[4] The Sorelmetal Book of Ductile Iron (2004). Canada: Rio Tinto Iron & Titanium Inc.

[5] Kusiak, A. & Kurasek, C. (2001). Data mining of printedcircuit board defects. IEEE Transactions on Robotics and Automation. 17, 191-196. DOI: 10.1109/70.928564.

[6] Sadoyan, H., Zakarian, A. & Mohanty, P. (2006). Data mining algorithm for manufacturing process control. Int J Adv Manuf Technol. 28, 342-350. DOI: 10.1007/s00170-004-2367-1.

[7] Shen, L., Tay, F.E.H., Qu, L. & Shen, Y. (2000). Fault diagnosis using Rough Sets Theory. Computers in Industry. 43, 61-72. DOI: 10.1016/S0166-3615(00)00050-6.

[8] Perzyk, M. & Soroczynski, A. (2008). Comparison of selected tools for generation of knowledge for foundry production. Archives of Foundry Engineering. 8, 263-266.

[9] Perzyk, M. & Soroczynski, A. (2010). Comparative Study of Decision Trees and Rough Sets Theory as Knowledge Extraction Tools for Design and Control of Industrial Processes. Proceedings of World Academy of Science, Engineering and Technology. 61, 234-239.

Archives of Foundry Engineering

The Journal of Polish Academy of Sciences

Journal Information

CiteScore 2016: 0.42

SCImago Journal Rank (SJR) 2016: 0.192
Source Normalized Impact per Paper (SNIP) 2016: 0.316

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