Empirical modeling of liquefied nitrogen cooling impact during machining Inconel 718

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

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.

• Language: English
• Keywords: cryogenic machining, cooling impact, Inconel 718, machine learning, adaptive neuro-fuzzy inference system, particle swarm optimization
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2020
• Number of pages: 16 str.
• Numbering: Vol. 10, iss. 10, art. 3603
• PID: 20.500.12556/RUL-133392
• UDC: 621.7+621.9:004.89
• ISSN on article: 2076-3417
• DOI: 10.3390/app10103603
• COBISS.SI-ID: 16781315
• Publication date in RUL: 25.11.2021

Record is a part of a journal

• Title: Applied sciences
• Shortened title: Appl. sci.
• Publisher: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 22.05.2020

Secondary language

• Language: Slovenian
• Keywords: kriogeno odrezavanje, hlajenje, strojno učenje, adaptivne mreže na osnovi mehkega identifikacijskega sistema, optimizacija z rojem delcev

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0157
• Name: Tehnološki sistemi za pametno proizvodnjo