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Title:Empirical modeling of liquefied nitrogen cooling impact during machining Inconel 718
Authors:ID Hriberšek, Matija (Author)
ID Berus, Lucijano (Author)
ID Pušavec, Franci (Author)
ID Klančnik, Simon (Author)
Files:.pdf RAZ_Hribersek_Matija_2020.pdf (2,37 MB)
MD5: D1874917312C2B0284B3038FB2C4614B
 
URL https://doi.org/10.3390/app10103603
 
Language:English
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Abstract: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 R2 = 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.
Keywords:cryogenic machining, cooling impact, Inconel 718, machine learning, adaptive neuro-fuzzy inference system, particle swarm optimization
Publication status:Published
Publication version:Version of Record
Submitted for review:03.03.2020
Article acceptance date:19.05.2020
Publication date:22.05.2020
Publisher:MDPI
Year of publishing:2020
Number of pages:Str. 1-16
Numbering:Letn. 10, št. 10, št. članka 3603
PID:20.500.12556/DKUM-81068 New window
UDC:621.7+621.9:004.89
ISSN on article:2076-3417
COBISS.SI-ID:16781315 New window
DOI:10.3390/app10103603 New window
Publication date in DKUM:14.07.2023
Views:564
Downloads:40
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
HRIBERŠEK, Matija, BERUS, Lucijano, PUŠAVEC, Franci and KLANČNIK, Simon, 2020, Empirical modeling of liquefied nitrogen cooling impact during machining Inconel 718. Applied sciences [online]. 2020. Vol. 10, no. 10,  članka 3603, p. 1–16. [Accessed 26 April 2025]. DOI 10.3390/app10103603. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=81068
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Record is a part of a journal

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

Document is financed by a project

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

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.

Secondary language

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


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