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Naslov:Prediction of the form of a hardened metal workpiece during the straightening process
Avtorji:ID Peršak, Tadej (Avtor)
ID Hernavs, Jernej (Avtor)
ID Vuherer, Tomaž (Avtor)
ID Belšak, Aleš (Avtor)
ID Klančnik, Simon (Avtor)
Datoteke:.pdf Persak-2023-Prediction_of_the_Form_of_a_Harden.pdf (10,52 MB)
MD5: CD6491FDD7C60EDD5D721D753EF2E97D
 
URL https://doi.org/10.3390/su15086408
 
Jezik:Angleški jezik
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FS - Fakulteta za strojništvo
Opis:In industry, metal workpieces are often heat-treated to improve their mechanical properties, which leads to unwanted deformations and changes in their geometry. Due to their high hardness (60 HRC or more), conventional bending and rolling straightening approaches are not effective, as a failure of the material occurs. The aim of the research was to develop a predictive model that predicts the change in the form of a hardened workpiece as a function of the arbitrary set of strikes that deform the surface plastically. A large-scale laboratory experiment was carried out in which a database of 3063 samples was prepared, based on the controlled application of plastic deformations on the surface of the workpiece and high-resolution capture of the workpiece geometry. The different types of input data, describing, on the one hand, the performed plastic surface deformations on the workpieces, and on the other hand the point cloud of the workpiece geometry, were combined appropriately into a form that is a suitable input for a U-Net convolutional neural network. The U-Net model’s performance was investigated using three statistical indicators. These indicators were: relative absolute error (RAE), root mean squared error (RMSE), and relative squared error (RSE). The results showed that the model had excellent prediction performance, with the mean values of RMSE less than 0.013, RAE less than 0.05, and RSE less than 0.004 on test data. Based on the results, we concluded that the proposed model could be a useful tool for designing an optimal straightening strategy for high-hardness metal workpieces. Our results will open the doors to implementing digital sustainability techniques, since more efficient handling will result in fewer subsequent heat treatments and shorter handling times. An important goal of digital sustainability is to reduce electricity consumption in production, which this approach will certainly do.
Ključne besede:sustraightening process, hardened workpiece, manufacturing, U-Net convolutional neural network, modeling, point cloud, digital sustainability
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:12.03.2023
Datum sprejetja članka:07.04.2023
Datum objave:09.04.2023
Založnik:MDPI
Leto izida:2023
Št. strani:Str. 1-19
Številčenje:Letn. 15, Št. 8, št. članka 6408
PID:20.500.12556/DKUM-87945 Novo okno
UDK:621.91:007.52
COBISS.SI-ID:148595971 Novo okno
DOI:10.3390/su15086408 Novo okno
ISSN pri članku:2071-1050
Datum objave v DKUM:02.04.2024
Število ogledov:74
Število prenosov:11
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
Področja:Ostalo
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Skupna ocena:(0 glasov)
Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.
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Gradivo je del revije

Naslov:Sustainability
Skrajšan naslov:Sustainability
Založnik:MDPI
ISSN:2071-1050
COBISS.SI-ID:5324897 Novo okno

Gradivo je financirano iz projekta

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

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:09.04.2023

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:postopek ravnanja, ojačan izdelek, proizvodnja, konvolucijske nevronske mreže, modeliranje, oblak točk, digitalna trajnost


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