Computer programme for prediction steel parameters after heat treatment

• Autorzy: Trzaska J. , Dobrzański L. A. , Jagiełło A.
• Języki publikacji: EN

Abstrakty

EN

Purpose: The purpose of this paper is presentation of the computer program for calculating the Continuous Cooling Transformation diagrams for constructional and engineering steels. Design/methodology/approach: The computer program uses the artificial neural networks for prediction steel properties after heat treatment. Input data are chemical composition and austenitizing temperature. Results of calculation consist of temperature of the beginning and the end of transformation in the cooling rate function, the volume fraction of structural components and hardness of steel cooled from austenitizing temperature with a fixed rate. Findings: The algorithm can be use in designing new chemical compositions of steels with assumed hardness after heat treatment. Research limitations/implications: The created method for designing chemical compositions is limited by ranges of mass concentrations of elements. The methodology demonstrated in the paper makes possibility to add new steels to the system. Practical implications: The method may be used in computer steel selection systems for machines parts manufactured from constructional or engineering steels subjected to heat treatment. Originality/value: The presented computer program can be used for selecting steel with required structure after heat treatment.

Słowa kluczowe

EN

computational materials science; artificial intelligence methods; neural networks; CCT diagrams

PL

komputerowa nauka o materiałach; metody sztucznej inteligencji; sieci neuronowe; diagramy CTPc

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2007
• Tom: Vol. 24, nr 2
• Strony: 171--174
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Trzaska J.

autor

Dobrzański L. A.

autor

Jagiełło A.

• Division of Materials Processing Technology, Management and Computer Techniques in Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18 a, 44-100 Gliwice, Poland,

Bibliografia

• [1] J.C. Zhao, M.R. Notis, Continuous cooling transformation kinetics versus isothermal transformation kinetics of steels: a phenomenological rationalization of experimental observations, Materials Science and Engineering R15 (1995) 135-207.
• [2] W.G. Vermulen, S. Van Der Zwaag, P. Morris, T. Weijer, Prediction of the continuous cooling transformation diagram of some selected steels using artificial neural networks, Steel Research 68 (1997) 72-79.
• [3] J. Wang, P.J. Van Der Wolk, S. Van Der Zwaag, Effects of Carbon Concentration and Cooling Rate on Continuous Cooling Transformations Predicted by Artificial Neural Network, ISIJ International 39 (1999) 1038-1046.
• [4] H.K.D.H. Bhadeshia, Neural Networks in Materials Science, ISIJ International 39 (1999) 966-1000.
• [5] L. Miaoquan, Ch. Dunjun, X. Aiming, Li. Long, An adaptive prediction model of grain size for the forging of Ti-6Al-4V alloy based on fuzzy neural networks, Journal of Materials Processing Technology 123 (2002) 377-381.
• [6] R. Kapoor, D. Pal, J.K. Chakravartty, Use of artificial neural networks to predict the deformation behavior of Zr-2.5Nb-0.5Cu, Journal of Materials Processing Technology 169/2 (2005) 199-205.
• [7] I.S. Jalham, A comparative study of some network approaches to predict the effect of the reinforcement content on the hot strength of Al-base composites Journal of Materials Processing Technology 166/3 (2005) 392-397.
• [8] W. Sitek, Employment of rough data for modelling of materials properties, Journal of Achievements in Materials and Manufacturing Engineering 21/2 (2007) 65-68.
• [9] L.A. Dobrzański, M. Sroka, J. Dobrzański, Application of neural networks to classification of internal damages in steels working in creep service, Journal of Achievements in Materials and Manufacturing Engineering 20 (2007) 303-306.
• [10] L.A. Dobrzański, J. Trzaska, Application of neural networks for prediction of hardness and volume fractions of structural components constructional steels cooled from the austenitizing temperature, Materials Science Forum 437-438 (2003) 359-362.
• [11] L.A. Dobrzański, J. Trzaska, Application of neural networks to forecasting the CCT diagram, Journal of Materials Processing Technology 157-158 (2004) 107-113.
• [12] L.A. Dobrzański, J. Trzaska, Application of neural network the prediction of continous cooling transformation diagrams, Computational Materials Science 30 (2004) 251-259.
• [13] L.A. Dobrzański, J. Trzaska, Application of neural networks for prediction of critical values of temperatures and time of the supercooled austenite transformations, Journal of Materials Processing Technology 155-156 (2004) 1950-1955.
• [14] J. Trzaska, L.A. Dobrzański, Modelling of transformations occuring during quenching in engineering steels, Machines Engineering 10/1-2 (2005) 41-60.
• [15] J. Trzaska, Methodology of the computer modelling of the supercooled austenite transformations of the constructional steels, PhD thesis -unpublished, Main Library of the Silesian University of Technology, Gliwice, 2002 (in Polish).