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
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
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
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
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.
EN 10088-4:2009. Stainless steels – Part 4: Technical delivery conditions for sheet/plate and strip of corrosion resisting steels for construction purposes
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
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.
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)
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
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
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
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
Lisjak, D., 2004. Application of various artificial intelligence methods in material selection. Doctoral dissertation, Faculty of Mechanical Engineering, University of Zagreb, Zagreb, Croatia.
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
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
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
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