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Predicting the mechanical properties of stainless steels using Artificial Neural Networks Cover

Predicting the mechanical properties of stainless steels using Artificial Neural Networks

Open Access
|May 2024

References

  1. Basheer, I. A., Hajmeer, M., 2000. Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43, 3–31, DOI: 10.1016/S0167-7012(00)00201-3
  2. Bursać, M., Jevtić, S., Tričković, G., 2021. Application of artificial neural networks for predictions of failure of railway signaling devices. Proceedings of Third International Conference “Transport for Today’s Society”, 14–16.10.2021, Bitola, North Macedonia, Faculty of Technical Sciences Bitola, 194–197. https://ttsconf.org/wp-content/uploads/2022/04/p45.pdf
  3. Ciocan, R., Petulescu, P., Ciobanu, D., Roth, D. J., 2000. The use of the neural networks in the recognition of the austenitic steel types. NDT&E International 33, 85–89, DOI: 10.1016/S0963-8695(99)00032-8
  4. Dobrzanski, L.A., Sitek, W., 1999, The modelling of hardenability using neural networks. Journal of Materials Processing Technology, 92–93, 8–14, DOI: 10.1016/S0924-0136(99)00174-0
  5. EN 1990:2002. Eurocode – Basis of structural design.
  6. https://www.phd.eng.br/wp-content/uploads/2015/12/en.1990.2002.pdf
  7. EN 1993-1-1:2005. Eurocode 3 – Design of steel structures – Part 1-1: General rules and rules for buildings
  8. https://www.phd.eng.br/wp-content/uploads/2015/12/en.1993.1.1.2005.pdf
  9. EN 1993-1-2:2005. Eurocode 3 – Design of steel structures – Part 1-2: General rules – structural fire design
  10. https://www.phd.eng.br/wp-content/uploads/2015/12/en.1993.1.2.2005.pdf
  11. EN 1993-1-3:2006. Eurocode 3 – Design of steel structures – Part 1-3: General rules – Supplementary rules for cold-formed members and sheeting
  12. https://www.phd.eng.br/wp-content/uploads/2015/12/en.1993.1.3.2006.pdf
  13. EN 1993-1-4:2006. Eurocode 3 – Design of steel structures – Part 1–4: General rules – Supplementary rules for stainless steels
  14. https://www.phd.eng.br/wp-content/uploads/2015/12/en.1993.1.4.2006.pdf
  15. EN 10088-1:2005. Stainless steels – Part 1: List of stainless steels
  16. https://standards.iteh.ai/catalog/standards/cen/952db42f-8160-4518-8932-c51bc76f8715/en-10088-1-2005
  17. EN 10088-2:2005. Stainless steels – Part 2: Technical delivery conditions for sheet/plate and strip of corrosion resisting steels for general purposes
  18. https://standards.iteh.ai/catalog/standards/cen/5da77ead-c665-4c16-a063-1b086a1543c2/en-10088-2-2005
  19. EN 10088-3:2005. Stainless steels – Part 3: Technical delivery conditions for semi-finished products, bars, rods, wire, sections and bright products of corrosion resisting steels for general purposes.
  20. https://standards.iteh.ai/catalog/standards/cen/4e6c80d2-c72d-42b3-aeae-2564ae23eb38/en-10088-3-2005
  21. EN 10088-4:2009. Stainless steels – Part 4: Technical delivery conditions for sheet/plate and strip of corrosion resisting steels for construction purposes
  22. https://standards.iteh.ai/catalog/standards/cen/a9506eec-011c-47b3-9313-6ec0bb544a7b/en-10088-4-2009
  23. EN 10088-5:2009. Stainless steels – Part 5: Technical delivery conditions for bars, rods, wire, sections and bright products of corrosion resisting steels for construction purposes
  24. https://standards.iteh.ai/catalog/standards/cen/affdda76-226f-42d3-b4c7-5d00eba17733/en-10088-5-2009
  25. Ivković, Dj., Arsić, D. Adamović, D., Nikolić, R., Mitrović, A., Bokuvka, O., 2024. Predicting the yield stress and tensile strength of two stainless steels using artificial intelligence. Proceedings of The 27th International Seminar of Ph.D. students - SEMDOK 2024, 05-07.02.2024, Western Tatras - Zuberec, Slovakia, 57–62.
  26. Jovanović, M., Lazić, V., Arsić, D., 2017. Material Science, Faculty of Engineering. University of Kragujevac, Kragujevac, Serbia, ISBN 978-86-6335-042-7. (in Serbian)
  27. Kim, D., 2023. Text Classification Based on Neural Network Fusion. Technical Journal, 17(3), 359–366, DOI: 10.31803/tg-20221228154330
  28. Knap, M., Lamut, J., Rozman, A., Falkus, J., 2008. The prediction of hardenability using neuronal networks. Archives of Metallurgy and Materials, 53(3), 761–766, DOI: 10.2478/amm-2014-0021
  29. Knap, M., Falkus, J., Rozman, A., Konopka, K., Lamut, J., 2014, The Prediction of Hardenability using Neural Networks. Archives of Metallurgy and Materials, 59(1), 133–136, DOI: 10.2478/amm-2014-0021
  30. Kusiak, J., Kuziak, R., 2002. Modelling of microstructure and mechanical properties of steel using the artificial neural network. Journal of Materials Processing Technology, 127(1), 115–121, DOI: 10.1016/S0924-0136(02)00278-9
  31. Lee, J-G., Jun, S., Cho, -W., Lee, H., Kim, G. B., Seo, J. B., Kim, N., 2017. Deep Learning in Medical Imaging: General Overview. Korean Journal of Radiology, 18(4), 570–584, DOI: 10.3348/kjr.2017.18.4.570
  32. Lisjak, D., 2004. Application of various artificial intelligence methods in material selection. Doctoral dissertation, Faculty of Mechanical Engineering, University of Zagreb, Zagreb, Croatia.
  33. Menasri, N., Aimeur, N., 2023. Faults diagnostics of cement draft fan using artificial neural network (ANN). Structural Integrity and Life, 23(1), 23–29. http://divk.inovacionicentar.rs/ivk/ivk23/023-IVK1-2023-NMNA.pdf
  34. Mukherjee, A., Schmauder, S., Ruhle, M., 1995. Artificial neural networks for the prediction of mechanical behavior of metal matrix composites. Acta Metall. Mater. 43(11), 4083–4091, https://edisciplinas.usp.br/pluginfile.php/5791436/mod_resource/content/1/Artigo%204.pdf
  35. Qamar, R., Zardari, B. A., 2023. Artificial Neural Networks: An Overview. Mesopotamian journal of Computer Science, 2023, 130–139, DOI: 10.58496/MJCSC/2023/015
  36. Sitek, W., Dobrzanski, 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: 10.1016/j.jmatprotec.2004.09.037
  37. Sitek, W., Trzaska, J., Gemechu, W. F., 2022. Modelling and Analysis of the Synergistic Alloying Elements Effect on Hardenability of Steel. Archives of foundry engineering, 4, 102–108, DOI: 10.24425/afe.2022.143957
  38. Sorić, J., Stanić, M., Lesičar, T., 2023. On neural network application in solid mechanics. Transactions of FAMENA, 47(2), 45–66, DOI: 10.21278/TOF.472053023
  39. Tylek, I., Kuchta, K., 2014. Mechanical properties of structural stainless steels. Technical Transactions Civil Engineering, 4-B(12), 59–80, https://www.ejournals.eu/Czasopismo-Techniczne/2014/Budownictwo-Zeszyt-4-B-(12)-2014/art/5743/
  40. Varenina, A., Malvić, T. Režić, M., 2018. Improved neural network model of assessment for interpretation of Miocene lithofacies in the Vukovar formation. Northern Croatia, Materials and Geoenvironment, 68(3), 145–156, DOI: 10.2478/rmzmag-2018-0029
DOI: https://doi.org/10.30657/pea.2024.30.21 | Journal eISSN: 2353-7779 | Journal ISSN: 2353-5156
Language: English
Page range: 225 - 232
Submitted on: Feb 29, 2024
Accepted on: Apr 15, 2024
Published on: May 26, 2024
Published by: Quality and Production Managers Association
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Djordje Ivković, Dušan Arsić, Dragan Adamović, Ružica Nikolić, Andjela Mitrović, Otakar Bokuvka, published by Quality and Production Managers Association
This work is licensed under the Creative Commons Attribution 4.0 License.