Modelling the Structural Steel Hardness Using Genetic Programming Method

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The aim of the paper is to demonstrate, how artificial intelligence methods, especially genetic ones, naturally combine with problems of material science. On the example of modelling a function showing how carbon concentration in steel changes its hardness it was shown how modern artificial intelligence methods can easily be adapted for solving problems of modern science. The paper presents the possibility of applying genetic programming to model properties of the steel.

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580-585

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October 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Z. Dąbrowski, P. Deuszkiewicz, Designing of high-speed machine shafts of carbon composites with highly nonlinear characteristics, Key Engineering Materials, Vol. 490 (2011) 76-82.

DOI: 10.4028/www.scientific.net/kem.490.76

Google Scholar

[2] L.A. Dobrzański, J. Trzaska, Application of neural networks for prediction of hardness and volume fractions of structural components in constructional steels cooled from the austenitizing temperature, Materials Science Forum, 437/4 (2003) 359–362.

DOI: 10.4028/www.scientific.net/msf.437-438.359

Google Scholar

[3] L.A. Dobrzański, J. Trzaska, Modelling of CCT diagrams for engineering and constructional steels, Journal of Materials Processing Technology, 192-193 (2007) 504-510.

DOI: 10.1016/j.jmatprotec.2007.04.099

Google Scholar

[4] L.A. Dobrzański, J. Trzaska, 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).

DOI: 10.1016/j.jmatprotec.2005.01.014

Google Scholar

[5] W. Sitek, J. Trzaska, L.A. Dobrzański, Modified Tartagli method for calculation of Jominy hardenability curve, Materials Science Forum, 575-578 (2008) 892-897.

DOI: 10.4028/www.scientific.net/msf.575-578.892

Google Scholar

[6] D. Janicki, High Power Diode Laser Cladding of Wear Resistant Metal Matrix Composite Coatings, Solid State Phenomena, Mechatronic Systems and Materials V, 199 (2013) 587-592 DOI: 10. 4028/www. scientific. net/SSP. 199. 587.

DOI: 10.4028/www.scientific.net/ssp.199.587

Google Scholar

[7] A. Lisiecki, Diode laser welding of high yield steel. Proc. of SPIE Vol. 8703, Laser Technology 2012: Applications of Lasers, 87030S (January 22, 2013), DOI: 10. 1117/12. 2013429.

DOI: 10.1117/12.2013429

Google Scholar

[8] W. Sitek, L.A. Dobrzański, Application of genetic methods in materials' design, Journal of Materials Processing Technology,  Vol. 164   (2005) 1607-1611.

DOI: 10.1016/j.jmatprotec.2005.01.005

Google Scholar

[9] W. Sitek, L.A. Dobrzański, J. Zaclona, The modelling of high-speed steels' properties using neural networks,  Journal of Materials Processing Technology, Vol. 157 (2004) 245-249.

DOI: 10.1016/j.jmatprotec.2004.09.037

Google Scholar

[10] W. Sitek, L.A. Dobrzański, Comparison of hardenability calculation methods of the heat-treatable constructional steels,  Journal of Materials Processing Technology, Vol. 64, Issue: 1-3  (1995)  117-126.

DOI: 10.1016/s0924-0136(96)02559-9

Google Scholar

[11] W. Sitek, L.A. Dobrzański, M. Krupiński, Computer aided method for evaluation of failure class of materials working in creep conditions,  Journal of Materials Processing Technology, Vol. 157 (2004) 102-106.

DOI: 10.1016/j.jmatprotec.2004.09.020

Google Scholar

[12] W. Sitek, A mathematical model of the hardness of high-speed steels, Transactions of Famena, Vol. 34, Issue: 3 (2010) 39-46.

Google Scholar

[13] P. Folęga, FEM analysis of the options of using composite materials in flexsplines, Archives of Materials Science and Engineering, 51(1) (2011) 55-60.

Google Scholar

[14] P. Folęga, G. Siwiec, Numerical analysis of selected materials for flexsplines, Archives of Metallurgy and Materials, 57(1) (2012) 185-191.

DOI: 10.2478/v10172-012-0008-5

Google Scholar

[15] A. Grządziela, Ship shock modeling of underwater explosion, Solid State Phenomena, Vol. 180 (2012) 288-296.

DOI: 10.4028/www.scientific.net/ssp.180.288

Google Scholar

[16] A. Grządziela, Ship impact modeling of underwater explosion, Journal of KONES Powertrain and Transport, Vol. 18 No. 2 (2011) 145-152.

Google Scholar

[17] R. Burdzik, Research on the influence of engine rotational speed to the vibration penetration into the driver via feet - multidimensional analysis, Journal of Vibroengineering 15 (4) (2013) 2114-2123.

Google Scholar

[18] J. Dziurdź, Modelling of the Toothed Gear Operations with the Application of the Analysis of the Gear Sliding Mesh Velocity Changes, Solid State Phenomena, Vol. 180 (2012) 200-206.

DOI: 10.4028/www.scientific.net/ssp.180.200

Google Scholar

[19] T. Tański, L.A. Dobrzanski, L. Cizek, L, Influence of heat treatment on structure and properties of the cast magnesium alloys, Advanced Materials Research 15-17 (2007) 491-496.

DOI: 10.4028/www.scientific.net/amr.15-17.491

Google Scholar

[20] A. Grządziela, Modeling of propeller shaft dynamics at pulse load, Polish Maritime Researches, Vol. 15 No. 4 (2008) 52 – 58.

DOI: 10.2478/v10012-007-0097-7

Google Scholar

[21] A. Grządziela, Diagnosis of naval gas turbine rotors with the use of vibroacoustic parameters, Polish Maritime Researches, Vol. 7 No. 3 (2000) 14-17.

Google Scholar

[22] L.A. Dobrzanski, T. Tański, J. Trzaska, Optimization of heat treatment conditions of magnesium cast alloys, Materials Science Forum 638-642 (2009) 1488-1493.

DOI: 10.4028/www.scientific.net/msf.638-642.1488

Google Scholar

[23] J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, The MIT Press, Cambridge, Massachusetts, (1992).

Google Scholar

[24] D.E. Goldberg, Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, (1989).

Google Scholar