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Tytuł artykułu

Modelling of foundary processes by artificial neural networks.

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Warianty tytułu
PL
Modelowanie procesów odlewniczych przy pomocy sztucznych sieci neuronowych.
Języki publikacji
EN
Abstrakty
EN
Applications of artificial neural networks related to foundary technology are presented, covering published and own research. The latter include prediction of ductile cast iron properties obtained in melting process, design of feeding systems for casting as well as identification of causes of defects in steel castings.
PL
Przedstawiono zastosowania sztucznych sieci neuronowych związane z technologią odlewania metali, opublikowane przez innych autorów oraz we własnych pracach z tej dziedziny. Te ostatnie dotyczą prognozowania właściwości żeliwa sferoidalnego uzyskiwanych w procesie wytopu, projektowania układów zasilających dla odlewów oraz identyfikacji przyczyn występowania wad w odlewach staliwnych w warunkach produkcyjnych.
Twórcy
  • Warsaw University of Technology, Institute of Materials Processing, Narbutta 85, 02-524 Warsaw, Poland
  • Warsaw University of Technology, Institute of Materials Processing, Narbutta 85, 02-524 Warsaw, Poland
  • Warsaw University of Technology, Institute of Materials Processing, Narbutta 85, 02-524 Warsaw, phone/fax (+48 22)8499906
Bibliografia
  • [1] K. Hatanaka, T. Tanaka, H. Kominami: Breakout forecasting system based on multiple neural networks for continuous casting in steel production. Fujitsu Scientific and Technical Journal, 29(1993)3, 265-270.
  • [2] K. Terashima, Y. Maesa, H. Namura: Learning-control of mould hardness in blow molding. Proc. 60th World Foundry Congress, Hague 1993.
  • [3] W. Chen, G. Duan, C. Ou: Neural network applied to predicting molten steel temperature profile from converter to continuous casting. Iron and Steel (China), 32(1997)8, 30-32.
  • [4] Y. Otsuka, M. Konishi: Neural network models and its applications to iron and steel making processes. Journal of the Iron and Steel Institute of Japan, 77(1991)10, 1539-1543.
  • [5] P.F. Bartelt, N.G. Bliss, J.S. Moberley: Application of artificial intelligence to power input control in the modern foundry, Transactions of the American Foundrymen's Society, 103(1996), 221-225.
  • [6] M.B. Brady, N.G. Bliss, H.D. Phillips: Integrated computer controls for the modern foundry meltshop. Proc. 51st Electric Furnace Conference, Washington 1993, 117-120.
  • [7] E.D. Larsen, D.E. Clark, H.B. Smartt, K.L. Moore: Intelligent control of cupola melting. Transactions of the American Foundrymen's Society, 103(1995), 215-219.
  • [8] W.H. Verduin, Y-H.Dr. Pao: Rapid foundry tooling system: A cutting-edge computer-aided design system. Proc. IEEE National Aerospace and Electronics Conference, Dayton 1993, 926-929.
  • [9] G. Wang, T.Y. Huang: Application of artificial neural networks in foundry industry. Proc. Third Asian Foundry Congress, Kyongju, South Korea, 1995, 424- 431.
  • [10] P.F. Bartelt, M.R. Grady, D. Dibble: Application of intelligent techniques for green sand control. Transactions of the American Foundrymen's Society, 104(1996), 1003-1009.
  • [11] R.B. Yao, C.X. Tang, G.X. Sun: Predicting gray cast iron properties with artificial neural network. Transactions of the American Foundrymen's Society, 104(1996), 635-642.
  • [12] N.G. Bliss, G.J. Gilbertson, J.E. Sweat: Advanced neural network control of the high impedance high voltage arc furnace. Proc. Conf. Near-Net-Shape Casting in the Minimills, Vancouver 1995, 99-105.
  • [13] S.A. Levy: Information from casting data. Proc. Light Metals 1991, New Orleans 1991, 909-914.
  • [14] T. Watanabe, K. Omura, M. Konishi, S. Watanabe, K. Furukawa: Mold level control in continuous caster by neural network model. ISIJ International, 39(1999)10, 1053-1060.
  • [15] L. Arafeh, H. Singh, P. Harpreet; S.K. Putatunda: Neuro fuzzy logic approach to material processing. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 29(1999)3, 362-370.
  • [16] S. Calcaterra, G. Campana, L. Tomesani: Prediction of Mechanical properties in spheroidal cast iron by neural networks. J. Mater. Proc. Technol. 104(2000)1,74-80.
  • [17] J. Huang, J.G. Conley, P. Callau: Alternative methods for porosity prediction in aluminium alloys. Development in CAD-CAM and CAE SAE Special Publications, 1336(1998), 93-103.
  • [18] A. Faessler, M. Loher: Quality control in die casting with neural networks. Proc. Int. Symp. on Neuro-Fuzzy Systems, IEEE USA, Laussanne 1996, 7.
  • [19] P.K.D.V. Yarlagadda, E.C.W. Chiang: Neural network system for prediction of process parameters in pressure die casting. J. Mater. Proc. Technol., 89-90(1999), 583-590.
  • [20] J.H. Zietsman, S. Kumar, J.A. Meech, I.V. Samarsekera, J.K. Brimacombe: Taper design in continuous billet casting using artificial neural networks, Ironmaking and Steelmaking, 25(1998)6, 476-483.
  • [21] C.C. Tai, J.C. Lin: Optimal position for the injection gate of a die casting die. J. Mater. Proc. Technoi, 86(1999)1-3, 87-100.
  • [22] M. Perzyk, A. Kochański: Prediction of ductile cast iron quality by artificial neural networks. J. Mater. Proc. Technol., 109(2001), 305-307.
  • [23] A. Kochański, M. Perzyk: Ductile cast iron classification by combined modelling. Acta Metallurgica Slovaca, 7(2001)3, 50-55.
  • [24] M. Perzyk, A. Kochański: New applications of artificial neural networks in foundry. Acta Metallurgica Slovaca, 7(2001)3, 380-384.
  • [25] A. Luovo, J. Alhainen, P. Eklund: Criterion functions for estimating the microstructure and mechanical properties of SG iron castings. Proc. 58th World Foundry Congress, Cracow 1992.
  • [26] M. Perzyk: Application of numerical simulation in preliminary design of feeding systems. Solidification of Metals and Alloys, 2(2000), 261-266.
  • [27] M. Perzyk, A. Kochański: Detection of causes of casting defects by artificial neural networks. Proc. Int. Conf. Advances in Production Engineering, Warsaw 2001.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BOS4-0005-0012
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