Next Article in Journal
Integration of Wavelet Denoising and HHT Applied to the Analysis of Bridge Dynamic Characteristics
Previous Article in Journal
Oxidative Stress Biomarkers in Erythrocytes of Captive Pre-Juvenile Loggerhead Turtles Following Acute Exposure to Methylmercury
Previous Article in Special Issue
Brain-Inspired Healthcare Smart System Based on Perception-Action Cycle
Open AccessArticle

Empirical Modeling of Liquefied Nitrogen Cooling Impact during Machining Inconel 718

1
Laboratory for Cutting Processes, Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva 6, 1000 Ljubljana, Slovenia
2
Intelligent Manufactoring Laboratory, Production Engineering Institute, Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia
3
Laboratory for Machining Processes, Production Engineering Institute, Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(10), 3603; https://doi.org/10.3390/app10103603
Received: 3 March 2020 / Revised: 14 May 2020 / Accepted: 19 May 2020 / Published: 22 May 2020
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 ( t e s t   R M S E ) = 0.2620 , and t e s t   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. View Full-Text
Keywords: cryogenic machining; cooling impact; Inconel 718; machine learning; adaptive neuro-fuzzy inference system; particle swarm optimization cryogenic machining; cooling impact; Inconel 718; machine learning; adaptive neuro-fuzzy inference system; particle swarm optimization
Show Figures

Figure 1

MDPI and ACS Style

Hribersek, M.; Berus, L.; Pusavec, F.; Klancnik, S. Empirical Modeling of Liquefied Nitrogen Cooling Impact during Machining Inconel 718. Appl. Sci. 2020, 10, 3603.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop