Modelling and Analysis of the Synergistic Alloying Elements Effect on Hardenability of Steel

• Autorzy: Sitek Wojciech , Trzaska Jacek , Gemechu W. F.

Identyfikatory

DOI

10.24425/afe.2022.143957

• Języki publikacji: EN

Abstrakty

EN

The paper presents a methodology of modeling relationships between chemical composition and hardenability of structural alloy steels using computational intelligence methods, that are artificial neural network and multiple regression models. Particularly, the researchers used unidirectional multilayer teaching method based on the error backpropagation algorithm and a quasi-newton methods. Based on previously known methodologies, it was found that there is no universal method of modeling hardenability, and it was also noted that there are errors related to the calculation of the curve. The study was performed on large set of experimental data containing required information on about the chemical compositions and corresponding Jominy hardenability curves for over 400 data steel heats with variety of chemical compositions. It is demonstrated that the full practical usefulness of the developed models in the selection of materials for particular applications with intended performance in the area of application.

Słowa kluczowe

EN

hardenability; artificial neural networks; multiple regression; steel alloy; modelling and simulation

PL

hartowność; sztuczne sieci neuronowe; regresja wielokrotna; stal; modelowanie i symulacja

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2022
• Tom: Vol. 22, iss. 4
• Strony: 102--108
• Opis fizyczny: Bibliogr. 37 poz., il., tab., wykr.

Twórcy

autor

Sitek Wojciech

• Silesian University of Technology, Department of Engineering Materials and Biomaterials Poland

• https://orcid.org/0000-0002-6564-7177

autor

Trzaska Jacek

• Silesian University of Technology, Department of Engineering Materials and Biomaterials Poland

• https://orcid.org/0000-0003-2297-3973

autor

Gemechu W. F.

• Silesian University of Technology, Department of Engineering Materials and Biomaterials Poland

• https://orcid.org/0000-0002-0602-3727

Bibliografia

• [1] Kobasko, N.I. (2018). Optimal hardenability steel for any size and form of machine components to increase their service life and decrease alloy elements in material. International Journal of Current Research. 10(02), 65867-65878.
• [2] Grange, R.A., Hribal, C.R. & Porter, L.F. (1977). Hardness of tempered marten site in carbon and low-alloy steels. Metallurgical and Materials Transactions A. 8, 1775-1785. DOI: doi.org/10.1007/BF02646882.
• [3] Bain, E.C., Paxton, H.W. (1961). Alloying Elements in Steel, 2nd Edition. Metals Park, Ohio: American Society for Metals.
• [4] Kobasko, N.I. (2012). Correlation between chemical composition of steel, optimal hardened layer, and optimal residual stress distribution. In N.I. Kobasko & K.N. Prabhu (Eds.), Film and Nucleate Boiling Processes, STP1534 (61-80). West Conshohocken, PA: ASTM International.
• [5] Grossmann, M.A. (1942). Hardenability calculated from chemical composition. The American Institute of Mining, Metallurgical, and Petroleum Engineers. 150, 227-255.
• [6] ASTM A-255-02 (2002). Standard test methods for determining hardenability of steel. ASTM International. https://doi.org/10.1520/A0255-02.
• [7] Sitek, W. & Trzaska, J. (2011). Numerical simulation of the alloying elements effect on steels’ properties. Journal of Achievements in Materials and Manufacturing Engineering. 45(1), 71-78.
• [8] Hart, G.L.W., Mueller, T., Toher, C. & Curtarolo, S. (2021). Machine learning for alloys. Nature Reviews Materials. 6, 730-755. https://doi.org/10.1038/s41578-021-00340-w.
• [9] Schmidt, J., Marques, M.R.G., Botti, S. & Marques, M.A.L. (2019). Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials. 5(83). https://doi.org/10.1038/s41524-019-0221-0.
• [10] Lambiase, F., Di Ilio, A.M., & Paoletti, A. (2013). Prediction of Laser Hardening by Means of Neural Network. In 8th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 18-20 July 2012 (pp. 181-186). Ischia, Italy: Elsevier Procedia. https://doi.org/10.1016/j.procir.2013.09.032.
• [11] Chong, ZS, Wilcox, S, & Ward, J. (2005). The Use of Artificial Intelligence in the Modelling and Heat Treatment Parameters Identification for Alloy-Steel Re-Heating Process. In Proceedings of the ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 1: 20th Biennial Conference on Mechanical Vibration and Noise, Parts A, B, and C, September 24-28, 2005 (pp. 607-613). Long Beach, California, USA: ASME. https://doi.org/10.1115/DETC2005-84802.
• [12] Hodge, J.M. & Orehoski, M.A. (1946). Relationship between hardenability and martensite in some low-alloy steels. The American Institute of Mining, Metallurgical, and Petroleum Engineers. 167, 502-512.
• [13] Mangonon, P.L. (1982). Relative hardenabilities and interaction effects of Mo and V in 4330 alloy steel. Metallurgical and Materials Transactions A. 13, 319-320. DOI: 10.1007/BF02643323.
• [14] Kramer, I.R., Hafner, R.H. & Toleman, S.L. (1944). Effect of Sixteen Alloying Elements on Hardenability of Steel, The American Institute of Mining, Metallurgical, and Petroleum Engineers. 158, 138-156.
• [15] Doane, D.V. (1979). Application of Hardenability Concepts in Heat Treatment of Steel. Journal of Heat Treating. 1, 5-30. DOI: doi.org/10.1007/BF02833206.
• [16] Doane, D.V. & Kirkaldy, J.S.G. (1978). Hardenability concepts with application to steels. Transactions of the Metallurgical Society of AIME. 12, 626-334.
• [17] Jatczak, C.F. (1973). Hardenability in high carbon steels. Metallurgical and Materials Transactions B. 4, 2267-2277.DOI: doi.org/10.1007/BF02669366.
• [18] Grange, R.A. (1973). Estimating the hardenability of carbon steels. Metallurgical and Materials Transactions B. 4, 2231-2244. DOI: doi.org/10.1007/BF02669363.
• [19] Crafts, W. & Lamont, J.L. (1944). Effects of some elements on hardenability. The American Institute of Mining, Metallurgical, and Petroleum Engineers. 1(11), 157-167.
• [20] Comstock, G.F. (1945). The influence of titanium on the hardenability of steel. Transactions of the Metallurgical Society of AIME. 12 (6), 148-150.
• [21] Moser, A. & Legat, A. (1969). Die Berechnung der Härtbarkeit aus der chemischen Zusammensetzung. HTM Härterei-technische Mitteilungen. 24(2), 100-104. (in German).
• [22] Sitek, W. & Jabłoński, A. (2015). The application of neural networks to analysis of the effects of chemical composition on hardenability of steel. Journal of Achievements in Materials and Manufacturing Engineering. 72(1), 32-38.
• [23] Sitek, W. & Irla, A. (2016). The Use of fuzzy systems for forecasting the hardenability of steel. Archives of Metallurgy and Materials. 61(2), 797-802. DOI:dx.doi.org/10.1515/amm-2016-0134.
• [24] Dobrzański, L.A. & Sitek, W. (1997). Comparison of hardenability calculation methods of the heat-treatable constructional steels. Journal of Materials Processing Technology. 64(1-3), 117-126. DOI: doi.org/10.1016/S0924-0136(96)02559-9.
• [25] Dobrzański, L.A. & Sitek, W. (1999). Designing of the chemical composition of constructional alloy steels. Journal of Materials Processing Technology. 89-90, 467-472. DOI:doi.org/10.1016/S0924-0136(99)00140-5.
• [26] Sitek, W. & Trzaska, J. (2021). Practical aspects of the design and use of the artificial neural networks in materials engineering. Metals 2021. 11(11), 1832. DOI:doi.org/10.3390/met11111832.
• [27] Dobrzański, L.A. & Sitek, W. (1999). The modelling of hardenability using neural networks. Journal of Materials Processing Technology. 92-93, 8-14. DOI:doi.org/10.1016/S0924-0136(99)00174-0.
• [28] Sitek, W., Trzaska, J., & Dobrzański, L.A. (2008). Modified Tartagli method for calculation of Jominy hardenability curve. Materials Science Forum. 575-578, 892-897. DOI:doi.org/10.4028/www.scientific.net/MSF.575-578.892.
• [29] Sitek, W. (2010). A mathematical model of the hardness of high-speed steels. Transactions of FAMENA. 34(3), 39-46.
• [30] Sitek, W., Dobrzański, L.A. & Zacłona, J. (2004). The modelling of high-speed steels’ properties using neural networks. Journal of Materials Processing Technology. 157-158, 245-249. DOI: doi.org/10.1016/j.jmatprotec.2004.09.037.
• [31] Sitek, W. & Dobrzański, L.A. (2005). Application of genetic methods in materials’ design. Journal of Materials Processing Technology. 164-165, 1607-1611. DOI:doi.org/10.1016/j.jmatprotec.2005.01.005.
• [32] Edelstahl Witten-Krefeld GmbH (2007). THYROFORT: Heat-treatable steels (Technical Information). Swiss Steel Group. https://www.swisssteel-group.com/fileadmin/user_upload/_SCHULUNG_/Sudafrika/Publications/heat_treatable_steel.pdf.
• [33] EN ISO 683-2:2018 (2018). Heat-treatable steels, alloy steels and free-cutting steels - Part 2: Alloy steels for quenching and tempering (ISO 683-2:2016). ISO Standards. https://www.iso.org/standard/70643.html.
• [34] Wever, F., Rose, A., Peter, W., Strassburg, W. & Rademacher, L. (1961). Atlas zur Wärmebehandlung der Stähle, Teil I and II. Max-Planck-Institut für Eisenforschung in Zusammenarbeit mit dem Werkstoffausschuss des Vereins Deutscher Eisenhüttenleute. Düsseldorf, Germany: Verlag Stahleisen m.b.H. (in German).
• [35] Rose, A. & Hougardy, H. (1972). Atlas zur Wärmebehandlung der Stähle, Band 2. Max-Planck-Institut für Eisenforschung in Zusammenarbeit mit dem Werkstoffausschuss des Vereins Deutscher Eisenhüttenleute. Düsseldorf, Germany: Verlag Stahleisen m.b.H. (in German)
• [36] Orlich, J., Rose, A. & Wiest, P. (1973). Atlas zur Wärmebehandlung der Stähle, Band 3: Zeit-Temperatur-Austenitisierung-Schaubilder. Max-Planck-Institut für Eisenforschung in Zusammenarbeit mit dem Werkstoffausschuss des Vereins Deutscher Eisenhüttenleute. Düsseldorf, Germany: Verlag Stahleisen m.b.H. (in German)
• [37] Orlich, J., & Pietrzeniuk, H. (1976). Atlas zur Wärmebehandlung der Stähle, Band 4: Zeit-Temperatur-Austenitisierung-Schaubilder, Teil 2. Max-Planck-Institut für Eisenforschung in Zusammenarbeit mit dem Werkstoffausschuss des Vereins Deutscher Eisenhüttenleute. Düsseldorf, Germany: Verlag Stahleisen m.b.H. (in German).

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)