Hybrid modelling methods in materials science - selected examples

• Autorzy: Sitek W. , Trzaska J.
• Języki publikacji: EN

Abstrakty

EN

Purpose: The paper presents selected examples of application of computational tools, including artificial intelligence methods to solve examples of tasks in the area of materials science. (i) Selection method of steel grade with required hardenability; (ii) Modelling of CCT diagrams for engineering and constructional steels; (iii) Application of neural networks for selection of steel with the assumed hardness after cooling from the austenitising temperature; (iv) Designing of high-speed steels chemical composition Design/methodology/approach: In the paper been applied a hybrid approach that combined application of various mathematical tools including artificial neural networks, linear regression and genetic algorithms to solve selected tasks from the area of materials science. Findings: Computer modelling and simulation make improvement of engineering materials properties possible, as well as prediction of their properties, even before the materials are fabricated, with the significant reduction of expenditures and time necessary for their investigation and application. Methods used in hybrid systems are complementary and disadvantages of one method are compensated by the advantages of another method. Practical implications: Solutions presented in the work, based on using the adequate material models may feature an interesting alternative in designing of the new materials with the required properties. The practical aspect has to be noted, resulting form the developed models, which may successfully replace the above mentioned technological investigations, consisting in one time selection of the chemical composition and heat treatment parameters and experimental verification of the newly developed materials to check of its properties meet the requirements. Originality/value: The presented approach to new materials design assumes the maximum possible limitation of carrying out the indispensable experiments, to take advantage of the existing experimental knowledge resources in the form of databases and most effective computer science tools, including neural networks and evolutionary algorithms.

Słowa kluczowe

EN

steels; artificial intelligence methods; modelling; simulation

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2012
• Tom: Vol. 54, nr 1
• Strony: 93--102
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Sitek W.

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

autor

Trzaska J.

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

Bibliografia

• [1] W. Sitek, J. Trzaska, L.A. Dobrzański, An artificial intelligence approach in designing new materials, Journal of Achievements in Materials and Manufacturing Engineering 17 (2006) 277-280.
• [2] W. Sitek, L.A. Dobrzański, Application of genetic methods in materials’ design, Journal of Materials Processing Technology 164 (2005) 1607-1611.
• [3] W. Sitek, Methodology of high-speed steels design using the artificial intelligence tools, Journal of Achievements in Materials and Manufacturing Engineering 39/2 (2010) 115-160.
• [4] L.A. Dobrzański, W. Sitek, Application of a neural network in modelling of hardenability of constructional steels, Journal of Materials Processing Technology 78/1 (1998) 59-66.
• [5] L.A. Dobrzański, W. Sitek, The modelling of hardenability using neural networks, Journal of Materials Processing Technology 92 (1999) 8-14.
• [6] W. Sitek, J. Trzaska, L.A. Dobrzański, Selection method of steel grade with required hardenability, Journal of Achievements in Materials and Manufacturing Engineering 17 (2006) 289-292.
• [7] L.A. Dobrzański, J. Trzaska, Application of neural network for the prediction of continuous cooling transformation diagrams, Computational Materials Science 30/3-4 (2004) 251-259.
• [8] 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).
• [9] J. Trzaska, L.A. Dobrzański, A. Jagiełło, Computer program for prediction steel parameters after heat treatment, Journal of Achievements in Materials and Manufacturing Engineering 24/2 (2007) 171-174.
• [10] J. Trzaska, L.A. Dobrzański, Modelling of CCT diagrams for engineering and constructional steels, Journal of Materials Processing Technology 192 (2007) 504-510.
• [11] J. Trzaska, L.A. Dobrzański, Application of neural networks for selection of steel with the assumed hardness after cooling from the austenitising temperature, Journal of Achievements in Materials and Manufacturing Engineering 16 (2006) 145-150.
• [12] 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 164-165 (2005) 1637-1643.
• [13] L.A. Dobrzański, J. Trzaska, Application of neural networks for prediction of hardness and volume fractions of structural components constructional steels cooled from the austenitising temperature, Materials Science Forum 437-438 (2003) 359-362.
• [14] W. Sitek, Methodology of high-speed steels design using the artificial intelligence tools, Journal of Achievements in Materials and Manufacturing Engineering 39/2 (2010) 115-160.
• [15] L.A. Dobrzański, A. Zarychta, M. Ligarski, E. Hajduczek, The role of Nb or Ti as alloying elements in W-Mo-V high speed steels, Division of Tool Materials and Computer Techniques in Metal Science, Silesian University of Technology, Gliwice, 1994.
• [16] L.A. Dobrzański, A. Zarychta, E. Hajduczek, M. Ligarski, Heat treatment of W-Mo-V i W-V high speed steels with Ti addition, Division of Tool Materials and Computer Techniques in Metal Science, Silesian University of Technology, Gliwice, 1997.
• [17] EN ISO 4957:2004, Tool steels, 2004.
• [18] http://www.erasteel.com/us/produits/hss.php.