Evaluation of chemical composition effect on materials properties using Al methods

• Autorzy: Sitek W. , Trzaska J. , Dobrzański L. A.
• Konferencja: 12th International Scientific Conference CAM3S'2006, 27-30th November 2006, Gliwice-Zakopane
• Języki publikacji: EN

Abstrakty

EN

Purpose: The paper presents the application of artificial neural network for evaluation of alloying elements effect on selected materials properties and austenite transformations during continuous cooling. Design/methodology/approach: Multi-layer feedforward neural networks with learning rule based on the error backpropagation algorithm were employed for modelling the steels properties. Then the neural networks worked out were employed for the computer simulation of the effect of particular alloying elements on the steels' properties. Findings: Obtained results show that neural network are useful in evaluation of synergic effect of alloying elements on selected materials properties when classical investigations' results do not provide evaluation of the effect of two or more alloying elements. Research limitations/implications: The results presented are valid in the ranges of mass concentrations of alloying elements presented in the paper. Practical implications: The worked out relationship may be used in computer systems of steels' designing for the heat-treated machine parts. Originality/value: The use of the neural networks as an tool for evaluation of the chemical composition effect on steels' properties.

Słowa kluczowe

EN

computational material science; artificial intelligence methods; materials design; mechanical properties; metallic alloys

PL

komputerowa nauka o materiałach; sztuczna inteligencja; metody sztucznej inteligencji; projektowanie materiałowe; właściwości mechaniczne; stopy metaliczne

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2007
• Tom: Vol. 20, nr 1-2
• Strony: 379--382
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Sitek W.

autor

Trzaska J.

autor

Dobrzański L. A.

• Division of Materials Processing Technology 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. Trzaska, W. Sitek, L.A. Dobrzanski, Selection method of steel grade with required hardenability, Worldwide Journal of Materials and Manufacturing Engineering, 17 (2006) 289-292.
• [2] L.A. Dobrzański, W. Sitek, Designing of the chemical composition of constructional alloy steels, Journal of Materials Processing Technology, 89-90 (1999) 467-472.
• [3] L.A. Dobrzański, W. Sitek, Comparison of hardenability calculation methods of the heat treatable constructional steels Journal of Materials Processing Technology, 64 (1997) 117-126.
• [4] L.A. Dobrzański, W. Sitek, Application of neural network in modelling of hardenability of constructional steels, Journal of Materials Processing Technology, 78 (1998) 59-66.
• [5] 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.
• [6] L.A. Dobrzański, J. Trzaska, Application of neural networks to forecasting the CCT diagram, Journal of Materials Processing Technology, Vol. 157-158, (2004) 107-113.
• [7] L.A. Dobrzański, J. Trzaska, Application of neural network for the prediction of continuous cooling transformation diagrams, Computational Materials Science, 30 (2004) 251-259.
• [8] 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.
• [9] J. Trzaska, L.A. Dobrzański, Application of neural networks for designing the chemical composition of steel with the assumed hardness after cooling from the austenitising temperature, Journal of Materials Processing Technology, Vols. 164-165 (2005) 1637-1643.
• [10] W. Sitek, L.A. Dobrzański, Application of genetic methods in materials' design, Journal of Materials Processing Technology, 164-165 (2005) 1607-1611.
• [11] W. Sitek, L.A. Dobrzański, J. Zacłona: The modeling of high-speed steels properties using neural networks', Journal of Materials Processing Technology, Vol. 157-158, (2004) 245-249.
• [12] L.A. Dobrzański, W. Sitek, M. Krupiński, J. Dobrzański: Computer aided method for evaluation of failure сlass of materials working in creep conditions, Journal of Materials Processing Technology, 157-158 (2004) 102-106.
• [13] L.A. Dobrzański, M. Kowalski, J. Madejski: Methodology of the mechanical properties prediction for the engineering steel products using the Artificial Intelligence tools, Journal of Materials Processing Technology, 164-165 (2005) 1500-1509.
• [14] L.A. Dobrzański, M. Drak, J. Trzaska: Corrosion resistance of the polymer matrix hard magnetic composite materials Nd-Fe-B, Journal of Materials Processing Technology, 164-165 (2005) 795-804.
• [15] W. Sitek, J. Trzaska, L.A Dobrzański: An artificial intelligence approach in designing new materials, Worldwide Journal of Achievements in Materials and Manufacturing Engineering, 17 (2006) 277- 280.