The Precipitation and Dissolution of Alloy Iron Carbides in Vacuum Carburization Processes for Automotive and Aircraft Applications - Part II

Abstract:

The article is dedicated to the experiments and tests on the phenomena of precipitation and dissolution of alloy iron carbides in vacuum carburization processes. Special attention has been paid to the possibility of using artificial neural networks to predict the speed of the processes examined. In the section below, we are presenting the precipitation phenomena taking place in vacuum carburization processes and the experiments that were conducted. Moreover, a qualitative and metallographic analysis of carbide phenomena was described together with the method of numerical modelling and predicting the processes with the use of artificial neural networks.

• Periodical: Advanced Materials Research (Volume 486)
• Pages: 303-308
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.486.303
• Online since: March 2012
• Authors: Piotr Kula, Emilia Wołowiec, Robert Pietrasik, Konrad Dybowski, Leszek Klimek
• Keywords: Carbide, Heat Treatment, Neural Network (NN), Vacuum Carburizing

References

[1] E. El-Kassas, R. Mackie and A. El-Sheikh, A: Computers and Structures 79, (2001), p.1687.

DOI: 10.1016/s0045-7949(01)00099-2

[2] L. Dobrzanski and J. Trzaska: J. of Materials Processing Technology 155-156 (2004), pp. (1950).

[3] M. Raffiq, G. Bugmann and D. Easterbrook: Computers and Structures 79 (2001), p.1541.

[4] Z. Sterjovski, D. Nolan, K. Carpenter, D. Dunne and J. Norrish: J. of Materials Processing Technology 170 (2005), p.536.

DOI: 10.1016/j.jmatprotec.2005.05.040

[5] G. Cybenko: Continuous valued neural networks with two hidden layers are sufficient. Tech. rep., Tufts University, Medford, MA. Dept of Computer Science (1988).

[6] M. Hagan, H. Demuth and M. Beale: Neural Networks Design. Boston: PWS Publishing Company (1996).

[7] D. Rumelhart, G. Hinton and R. Wiliams: Learning internal representations by error propagation. Cambridge, MA: Explorations in the Microstructure of Cognition MIT Press (1986).

[8] S. Wang: Computers ind. Engineering Vol. 32, No. 1 (1997), p.67.

[9] K. Hornik, M. Stinchcombe and H. White: Neural Networks 115, Vol. 2 (1989), p.359.

[10] P. Kula, R. Pietrasik, and K. Dybowski: Journal of Materials Processing Technology, 164-165 (2005), p.876.

DOI: 10.1016/j.jmatprotec.2005.02.145

[11] M. Korecki, P. Kula, and J. Olejnik: Industrial Heating, 3 (2011).

[12] S. Malinov and W. Sha: Computational Materials Science 28 (2003), p.179.

[13] F. Rosenblatt: Psychological Review 65 (1958), p.386.

[14] D. Patterson: Artificial Neural Networks, Prentice Hall, Singapore, (1996).

[15] C. Bishop: Neural Networks for Pattern Recognition, Oxford University, (1995).

[16] M. Zhang and P. Kelly: Acta Materialia, Vol. 46, No. 13 (1998), p.4617.

[17] R. Heckel and H. Paxton: Trans. Am. Soc. Metals 53 (1961), p.539.

[18] H. Aaronson: Decomposition of austenite by diffusional processes, New York, (1962).

[19] A. Schneider and G. Inden: Computer Coupling Diagrams and Thermochemistry 31 (2007), p.141.