Neural network analysis of tensile strength of austempered ductile iron

• Autorzy: Ławrynowicz Z. , Dymski S. , Trempczyńska-Łent M. , Giętka T.
• Języki publikacji: EN

Abstrakty

EN

The neural technique was applied to the analysis of the ultimate tensile strength and additionally the yield strength of austempered ductile iron (ADI). Austempered ductile iron is an excellent material and it possesses attractive properties as high strength, ductility and toughness. This paper begins with an introduction to neural networks and demonstrates the ability of the method to investigate new phenomena in cases where the information cannot be accessed experimentally. The model allows the strength properties to be estimated as a function of heat treatment parameters and the chemical composition. A 'committee' model was used to increase the accuracy of the predictions. The model was validated by comparison its predictions with data of tensile tests experiments on austempered samples of ductile cast iron. The model successfully reproduces experimentally determined ultimate tensile strength and it can be exploited in the predictions of both ultimate and yield strength and in the design of chemical composition of cast irons and their heat treatments.

Słowa kluczowe

EN

tensile strength; neural networks; austempering; austempered ductile iron (ADI)

PL

wytrzymałość na rozciąganie; sieci neuronowe; hartowanie izotermiczne; żeliwo sferoidalne

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2007
• Tom: Vol. 7, iss. 3
• Strony: 99--104
• Opis fizyczny: Bibliogr. 10 poz., rys., tab.

Twórcy

autor

Ławrynowicz Z.

autor

Dymski S.

autor

Trempczyńska-Łent M.

autor

Giętka T.

• University of Technology and Life Sciences in Bydgoszcz, Mechanical Engineering Faculty, av. Kaliskiego 7, 85-796 Bydgoszcz, Poland,

Bibliografia

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• [4] S. Pietrowski, Nodular cast iron of bainitic ferrite structure with austenite or bainitic structure, Archives of Materials Science, vol. 18, No.4 (1997) 253-273. (in Polish).
• [5] S.E. Guzik, Austempered cast iron as a modern constructional material, Inżynieria Materiałowa, nr 6 (2003) 677-680. (in Polish).
• [6] D.C.J. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge, 1-613, 2001.
• [7] H.K.D.H. Bhadeshia, Neural networks in materials Science, ISIJ International, Vol.39, No 10 (1999) 966-979.
• [8] D. J. C. MacKay, Bayesian nonlinear modelling with neural networks. In Mathematical Modelling of Weld Phenomena -3 editor, H. Cerjak and H. K. D. H Bhadeshia, pages 359-389, London, U.K., 1997. The Institute of Materials.
• [9] Z. Ławrynowicz, S. Dymski, Estimation of the amount of retained austenite by neural network in austempered ductileiron (ADI), Archives of Foundry Engineering, PAN, Vol.6, No 19, (2006) 183-188. (in Polish).
• [10] M.A. Yescas, H.K.D.H. Bhadeshia, D.J. Mac Kay, Materials Science and Engineering, A311 (2001) 162-173.