Vaš brskalnik ne omogoča JavaScript!
JavaScript je nujen za pravilno delovanje teh spletnih strani. Omogočite JavaScript ali pa uporabite sodobnejši brskalnik.
Nacionalni portal odprte znanosti
Odprta znanost
DiKUL
slv
|
eng
Iskanje
Brskanje
Novo v RUL
Kaj je RUL
V številkah
Pomoč
Prijava
Empirical modeling of liquefied nitrogen cooling impact during machining Inconel 718
ID
Hriberšek, Matija
(
Avtor
),
ID
Berus, Lucijano
(
Avtor
),
ID
Pušavec, Franci
(
Avtor
),
ID
Klančnik, Simon
(
Avtor
)
PDF - Predstavitvena datoteka,
prenos
(2,36 MB)
MD5: 032972D3CEFA3FCA9A77529AF11BD9A2
URL - Izvorni URL, za dostop obiščite
https://www.mdpi.com/2076-3417/10/10/3603
Galerija slik
Izvleček
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
Število ogledov:
649
Število prenosov:
161
Metapodatki:
Citiraj gradivo
Navadno besedilo
BibTeX
EndNote XML
EndNote/Refer
RIS
ABNT
ACM Ref
AMA
APA
Chicago 17th Author-Date
Harvard
IEEE
ISO 690
MLA
Vancouver
:
Kopiraj citat
Objavi na:
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
Podobna dela
Podobna dela v RUL:
Podobna dela v drugih slovenskih zbirkah:
Nazaj