20.500.12556/RUL-133392 Empirical modeling of liquefied nitrogen cooling impact during machining Inconel 718 This paper explains liquefied nitrogen’s cooling ability on a nickel super alloy called Inconel 718. A set of experiments was performed where the Inconel 718 plate was cooled by a moving liquefied nitrogen nozzle with changing the input parameters. Based on the experimental data, the empirical model was designed by an adaptive neuro-fuzzy inference system (ANFIS) and optimized with the particle swarm optimization algorithm (PSO), with the aim to predict the cooling rate (temperature) of the used media. The research has shown that the velocity of the nozzle has a significant impact on its cooling ability, among other factors such as depth and distance. Conducted experimental results were used as a learning set for the ANFIS model’s construction and validated via k-fold cross-validation. Optimization of the ANFIS’s external input parameters was also performed with the particle swarm optimization algorithm. The best results achieved by the optimized ANFIS structure had test root mean squared error (test RMSE) = 0.2620, and test R$^2$ = 0.8585, proving the high modeling ability of the proposed method. The completed research contributes to knowledge of the field of defining liquefied nitrogen’s cooling ability, which has an impact on the surface characteristics of the machined parts. cryogenic machining cooling impact Inconel 718 machine learning adaptive neuro-fuzzy inference system particle swarm optimization kriogeno odrezavanje hlajenje strojno učenje adaptivne mreže na osnovi mehkega identifikacijskega sistema optimizacija z rojem delcev true false true Angleški jezik Slovenski jezik Članek v reviji 2021-11-25 08:49:06 2021-11-25 08:49:07 2022-09-12 04:16:08 0000-00-00 00:00:00 2020 0 0 16 str. iss. 10, art. 3603 Vol. 10 May 2020 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 621.7+621.9:004.89 2076-3417 10.3390/app10103603 16781315 RAZ_Hribersek_Matija_2020.pdf RAZ_Hribersek_Matija_2020.pdf 1 032972D3CEFA3FCA9A77529AF11BD9A2 b284157443df5071a00cefdc3df6792a6fcb0f77736bb626ad821875fbd4143d 579ccdaa-4dc4-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=151421 https://www.mdpi.com/2076-3417/10/10/3603 0 4cc72b34-4dc4-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=151420 Fakulteta za strojništvo 0 0 0