Empirical modeling of liquefied nitrogen cooling impact during machining Inconel 718

Hriberšek, Matija (avtor), Berus, Lucijano (avtor), Pušavec, Franci (avtor), Klančnik, Simon (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: cryogenic machining, cooling impact, Inconel 718, machine learning, adaptive neuro-fuzzy inference system, particle swarm optimization
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2020
• Št. strani: 16 str.
• Številčenje: Vol. 10, iss. 10, art. 3603
• PID: 20.500.12556/RUL-133392
• UDK: 621.7+621.9:004.89
• ISSN pri članku: 2076-3417
• DOI: 10.3390/app10103603
• COBISS.SI-ID: 16781315
• Datum objave v RUL: 25.11.2021

Gradivo je del revije

• Naslov: Applied sciences
• Skrajšan naslov: Appl. sci.
• Založnik: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 22.05.2020

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: kriogeno odrezavanje, hlajenje, strojno učenje, adaptivne mreže na osnovi mehkega identifikacijskega sistema, optimizacija z rojem delcev

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P2-0157
• Naslov: Tehnološki sistemi za pametno proizvodnjo