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Multistage discretization and clustering in multivariable classification of the impact of alloying elements on properties of hypoeutectic silumin

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The properties of hypoeutectic Al–Si alloy (silumin) with the addition of elements such as Cr, Mo, V and W are described. Changes in silumin microstructure under the impact of these elements result in a change of the mechanical properties. The research includes presentation of procedure for the acquisition of knowledge about these changes directly from experimental results using mixed data mining techniques. The procedure for analyzing small sets of experimental data for multistage, multivariate and multivariable models has been developed. Its use can greatly simplify such research in the future. An interesting achievement is the development of a voting procedure based on the results of classification trees and cluster analysis.
Rocznik
Strony
114--126
Opis fizyczny
Bibliogr. 33 poz., rys., tab., wykr.
Twórcy
  • AGH University of Science and Technology, Krakow, Poland
  • Foundry Research Institute, Zakopiańska 73, Krakow, Poland
  • AGH University of Science and Technology, Krakow, Poland
  • Foundry Research Institute, Zakopiańska 73, Krakow, Poland
  • Lodz University of Technology, Lodz, Poland
  • Lodz University of Technology, Lodz, Poland
  • Foundry Research Institute, Zakopiańska 73, Krakow, Poland
Bibliografia
  • [1] H. Okamoto, Al-Cr (Aluminum-Chromium), J. Phase Equilibria Diffus. 29 (1) (2008) 111–112.
  • [2] Alloy Phase Diagrams, ASM Handbook, vol. 3, 1992.
  • [3] H. Okamoto, Al-Mo (Aluminum-Molybdenum), J. Phase Equilibria Diffus. 31 (5) (2010) 492–493.
  • [4] T. Szymczak, G. Gumienny, T. Pacyniak, Effect of Cr andWon the crystallization process, the microstructure and properties of hypoeutectic silumin to pressure die casting, Arch. Foundry Eng. 16 (3) (2016) 109–114.
  • [5] T. Szymczak, G. Gumienny, T. Pacyniak, Effect effect of vanadium and molybdenum on the crystallization, microstructure and properties of hypoeutectic silumin, Arch. Foundry Eng. 15 (4) (2015) 81–86.
  • [6] T. Szymczak, G. Gumienny, K. Walas, T. Pacyniak, Effect of tungsten and molybdenum on the crystallization, microstructure and properties of silumin 226, Arch. Foundry Eng. 15 (3) (2015) 61–66.
  • [7] Z. Gorny, S. Kluska-Nawarecka, D. Wilk-Kolodziejczyk, Heuristic models of the toughening process to improve the properties of non-ferrous metal alloys, Arch. Metall. Mater. 58 (3) (2013) 849–852.
  • [8] W. Warmuzek, K. Regulski, A procedure of in situ identification of the intermetallic AlTMSi phase precipitates in the microstructure of the aluminum alloys, Pract. Metallogr. 48 (12) (2011) 660–683.
  • [9] K. Regulski, J. Jakubski, A. Opaliński, M. Brzeziński, M. Głowacki, The prediction of moulding sand moisture content based on the knowledge acquired by data mining techniques, Arch. Metall. Mater. 61 (3) (2016) 1363–1368.
  • [10] B. Mrzyglod, A. Kowalski, I. Olejarczyk-Wozenska, H. Adrian, M. Głowacki, A. Opaliński, Effect of heat treatment parameters on the formation of ADI microstructure with additions of Ni, Cu, Mo, Arch. Metall. Mater. 60 (3) (2015) 1941–1948.
  • [11] S. Kluska-Nawarecka, D. Wilk-Kołodziejczyk, K. Regulski, Practical aspects of knowledge integration using attribute tables generated from relational databases, in: R Katarzyniak, et al. (Eds.), Semantic Methods, SCI 381, Springer-Verlag, Berlin; Heidelberg, 2011 13–22.
  • [12] PN EN 1706, Aluminum and aluminum alloy. Castings. Chemical composition and mechanical properties, in Polish, 2011.
  • [13] W.S. Cleveland, The Elements of Graphing Data, Wadsworth, Monterey, CA, 1985.
  • [14] T. Hill, P. Lewicki, STATISTICS: Methods and Applications, StatSoft, Tulsa, OK, 2007.
  • [15] I. Witten, E. Frank, New York, in: Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2000.
  • [16] A. Macioł, P. Macioł, S. Je¸ drusik, J. Lelito, The new hybrid rulebased tool to evaluate processes in manufacturing, Int. J. Adv. Manuf. Technol. 79 (9-12) (2015) 1733–1745.
  • [17] G. Rojek, K. Regulski, D. Wilk-Kołodziejczyk, S. KluskaNawarecka, K. Jaśkowiec, A. Smolarek-Grzyb, Methods of computational intelligence in the context of quality assurance in foundry products, Arch. Foundry Eng. 16 (2) (2016) 11–16.
  • [18] F. Kara, K. Aslantaş, A. Çiçek, Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network, Appl. Soft Comput. 38 (2016) 64–74.
  • [19] F. Kara, K. Aslantaş, A. Çiçek, ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steel, Neural Comput. Appl. 26 (1) (2015) 237–250.
  • [20] O. Onal, A.U. Ozturk, Artificial neural network application on microstructure–compressive strength relationship of cement mortar, Adv. Eng. Softw. 41 (2) (2010) 165–169.
  • [21] K. Regulski, D. Wilk-Kołodziejczyk, G. Gumienny, Comparative analysis of the properties of the nodular cast iron with carbides and the austempered ductile iron with use of the machine learning and the support vector machine, Int. J. Adv. Manuf. Technol. 87 (1) (2016) 1077–1093.
  • [22] A. Glowacz, Z. Glowacz, Diagnostics of stator faults of the single-phase induction motor using thermal images, MoASoS and selected classifiers, Measurement 93 (2016) 86–93.
  • [23] A. Glowacz, Fault diagnostics of DC motor using acoustic signals and MSAF-RATIO30-EXPANDED, Arch. Electr. Eng. 65 (4) (2016) 733–744.
  • [24] D. Wilk-Kołodziejczyk, B. Kacprzyk, G. Gumienny, K. Regulski, G. Rojek, B. Mrzygłód, Approximation of ausferrite content in the compacted graphite iron with the use of combined techniques of data mining, Arch. Foundry Eng. 17 (3) (2017) 117–122.
  • [25] S.D. Bay, Multivariate discretization of continuous variables for set mining, KDD'00, in: Proceedings of the sixth ACM SIGKDD international conference on Knowledge Discovery and data mining, 2000, 315–319.
  • [26] P. Selemela, D.J. Plessis du, A comparative analysis of urban growth and development in traditional authority and nontraditional areas: the case of Rustenburg and Mahikeng municipalities in the North West Province South Africa, in: Urban Forum, Springer, 2016.
  • [27] I.A. Kruglov, A. Mishulina, Neural network modeling of Victor multivariable functions in ill-posed approximation problems, J. Comput. Syst. Sci. Int. 52 (4) (2013) 503–518.
  • [28] P. Jarosz, J. Kusiak, S. Małecki, P. Morkisz, P. Oprocha, W. Pietrucha, Ł. Sztangret, Metamodeling and optimization of a blister copper two-stage production process, JOM 68 (6) (2016) 1535–1540.
  • [29] D. Cruz, D.A. Talbert, W. Eberle, J. Biernacki, A neural network approach for predicting microstructure development In cement, in: Int'l Conf. Artificial Intelligence ICAI, 2016.
  • [30] V. Sundararaghavan, N. Zabaras, Classification and reconstruction of three-dimensional microstructures using support vector machines, Comput. Mater. Sci. 32 (2) (2005) 223–239.
  • [31] C. Olaru, L. Wehenkel, A complete fuzzy decision tree technique, Fuzzy Sets Syst. 138 (2003) 221–254.
  • [32] Z. Gronostajski, M. Kaszuba, M. Hawryluk, M. Zwierzchowski, A review of the degradation mechanisms of the hot forging tools, Arch. Civil Mech. Eng. 14 (4) (2014) 528–539.
  • [33] A. Milenin, M. Pernach, Ł. Rauch, R. Kuziak, T. Zygmunt, M. Pietrzyk, Modelling and optimization of the manufacturing chain for rails, Procedia Eng. 207 (2017) 2101–2106.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-613fa56a-af6b-41bd-a19e-cff159dcc592
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