[
1. Zhang R., Shao Z., Lin.: A review on modelling techniques for formability prediction of sheet metal forming. International Journal of Lightweight Materials and Manufacture 3 (2018) 115-125.
]Search in Google Scholar
[
2. Slota J., Jurcisin M., Spisak E.: Experimental and numerical analysis of local mechanical properties of drawn part. Key Engineering Materials 586 (2014) 245-248.
]Search in Google Scholar
[
3. Nielsen C.V., Bay N.: Review of friction modeling in metal forming processes. Jornal of Materials Processing Technology 255 (2018) 234-241.
]Search in Google Scholar
[
4. Trzepiecinski T., Lemu H.G.: Recent developments and trends in the friction testing for conventional sheet metal forming and Incremental sheet forming. Metals 10 (2020) 47.
]Search in Google Scholar
[
5. Wankhede P., Suresh K.: A review on the evaluation of formability in sheet metal forming. Advances in Materials and Processing Technologies 6 (2020) 458-485.10.1080/2374068X.2020.1731229
]Search in Google Scholar
[
6. Shisode M., Hazrati J., Mishra T., de Rooij M., ten Horn C., van Beeck J., van den Boogaard T.: Modeling boundary friction of coated sheets in sheet metal forming. Tribology International 153 (2021) 106554.
]Search in Google Scholar
[
7. Xu Z., Huang J., Mao M., Peng L., Lai X.: An investigation on the friction in a micro sheet metal roll forming processes considering adhesion and ploughing. Journal of Materials Processing Technology 285 (2020) 116790.
]Search in Google Scholar
[
8. Wang C., Ma R., Zhao J., Zhao J.: Calculation method and experimental study of coulomb friction coefficient in sheet metal forming. Journal of Manufacturing Processes 27 (2017) 126-137.
]Search in Google Scholar
[
9. Wang W., Zhao Y., Wang Z., Hua M., Wei X.: A study on variable friction model in sheet metal forming with advanced high strength steels. Tribology International 93 (2016) 17-28.
]Search in Google Scholar
[
10. Shisoide M.P., Hazrati J., Mishra T., de Rooij M., van den Boogaard T.: Modeling mixed lubrication friction for sheet metal forming applications. Procedia Manufacturing 47 (2020) 586-590.
]Search in Google Scholar
[
11. Świerczyńska A., Fydrych D., Landowski M., Rogalski G., Łabanowski, J. Hydrogen embrittlement of X2CrNiMoCuN25-6-3 super duplex stainless steel welded joints under cathodic protection. Construction and Building Materials 238 (2020) 117697.
]Search in Google Scholar
[
12. Rogalski G., Świerczyńska A., Landowski M., Fydrych D. Mechanical and microstructural characterization of TIG welded dissimilar joints between 304L austenitic stainless steel and Incoloy 800HT nickel alloy. Metals 10 (2020) 559.
]Search in Google Scholar
[
13. Argatov I.I., Chai Y.S. An artificial neural network supported regression model for wear rate. Tribology International 138 (2019) 211-214.
]Search in Google Scholar
[
14. Dragan A.: Neural network prediction of brake friction materials wear. Wear 268 (2010) 117-125.
]Search in Google Scholar
[
15. Jurkovic M., Jurkovic Z., Buljan S.: The tibological state test in metal forming processes using experiment and modelling. Journal of Achievements in Materials and Manufacturing Engineering 18 (2006) 383-386.
]Search in Google Scholar
[
16. Rapetto M.P., Almqvist A., Larsson R., Lugt P.M.: On the influence of surface roughness on real area of contact in normal, dry, friction free, rough contact by using a neural network. Wear 266 (2009) 592-595.
]Search in Google Scholar
[
17. Grymek S., Druet K., Łubiński J.I.: Perspektywy obliczeń neuronowych w inżynierii łożyskowania. Tribologia 33 (2002) 227-237.
]Search in Google Scholar
[
18. Trzepieciński T., Lemu H.G.: Application of genetic algorithms to optimize neural networks for selected tribological tests. Journal of Mechanical Engineering and Automation 2 (2012) 69-76.
]Search in Google Scholar
[
19. Frangu L., Ripa M.: Artificial neural networks applications in Tribology – a survey. NIMIASC2001 – 2001 NATO Advanced Study Institute on Neural Networks for Instrumentation, Measurement and Related Applications: Study Cases Crema, Italy, 9–20 October 2001.
]Search in Google Scholar
[
20. Trzos M.: Tendencje rozwojowe w modelowaniu zjawisk i procesów tribologicznych. Zagadnienia Eksploatacji Maszyn 151 (2007) 73-87.
]Search in Google Scholar
[
21. EN 10130:2009. Cold-rolled low carbon steel flat products for cold forming. Technical delivery conditions. European Commitee for Standardization, Brussels, 2009.
]Search in Google Scholar
[
22. Sedlaček M., Vilhena L.M.S., Vižintin J.: Surface topography modeling for reduced friction. Strojniški Vestnik-Journal of Mechanical Engineering 57 (2011) 674-680.10.5545/sv-jme.2010.140
]Search in Google Scholar
[
23. Ferrero Bermejo J., Gómez Fernández J.F., Olivencia Polo F., Crespo Márquez A.: A review of the use of artificial neural network models for energy and reliability prediction. A study of the solar PV, hydraulic and wind energy sources. Applied Sciences 9 (2019) 1844.
]Search in Google Scholar
[
24. Esperacia-Alcázar A.I., Moravec J.: Fitness approximation for bot evolution in genetic programming. Soft Computing 17 (2013) 1479–1487.
]Search in Google Scholar
[
25. Katoch S., Chauhan S.S., Kumar V. A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications 80 (2021) 8091–8126.10.1007/s11042-020-10139-6759998333162782
]Search in Google Scholar
[
26. Winiczenko R.: Algorytmy genetyczne i ich zastosowania. Ekonomia, Zarządzanie, Informatyka. Marketing 1 (2008) 107-110.
]Search in Google Scholar
[
27. Cheng B, Titterington D.M.: Neural networks: A review from a statistical perspective. Statistical Science 9 (1994) 2-54.
]Search in Google Scholar
[
28. Manual of Statistica Neural Networks Software. StatSoft Inc., Tulsa, 1998.
]Search in Google Scholar
