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Naslov:Optimizing laser cutting of stainless steel using latin hypercube sampling and neural networks
Avtorji:ID Šket, Kristijan (Avtor)
ID Potočnik, David (Avtor)
ID Berus, Lucijano (Avtor)
ID Hernavs, Jernej (Avtor)
ID Ficko, Mirko (Avtor)
Datoteke:.pdf 1-s2.0-S0030399224016785-main.pdf (3,38 MB)
MD5: 35F8348F05CFAF548C77214BAB0D8A38
 
URL https://www.sciencedirect.com/science/article/pii/S0030399224016785?via%3Dihub
 
Jezik:Angleški jezik
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FS - Fakulteta za strojništvo
Opis:Optimizing cutting parameters in fiber laser cutting of austenitic stainless steel is challenging due to the complex interplay of multiple variables and quality metrics. To solve this problem, Latin hypercube sampling was used to ensure a comprehensive and efficient exploration of the parameter space with a smaller number of trials (185), coupled with feedforward neural networks for predictive modeling. The networks were trained with a leave-oneout cross-validation strategy to mitigate overfitting. Different configurations of hidden layers, neurons, and training functions were used. The approach was focused on minimizing dross and roughness on both the top and bottom areas of the cut surfaces. During the testing phase, an average MSE of 0.063 and an average MAPE of 4.68% were achieved by the models. Additionally, an experimental test was performed on the best parameter settings predicted by the models. Initial modelling was conducted for each quality metric individually, resulting in an average percentage difference of 1.37% between predicted and actual results. Grid search was also per formed to determine an optimal input parameter set for all outputs, with predictions achieving an average ac curacy of 98.34%. Experimental validation confirmed the accuracy and robustness of the model predictions, demonstrating the effectiveness of the methodology in optimizing multiple parameters of complex laser cutting processes.
Ključne besede:laser cutting optimization, cut surface quality, dross formation, Latin hypercube sampling, feedforward neural network
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:23.10.2024
Datum sprejetja članka:24.11.2024
Datum objave:30.11.2024
Založnik:Elsevier
Leto izida:2025
Št. strani:str. 1-10
Številčenje:Vol. 182, pt. B, [article no.] 112220
PID:20.500.12556/DKUM-91520 Novo okno
UDK:621.9:004.8
COBISS.SI-ID:220295427 Novo okno
DOI:10.1016/j.optlastec.2024.112220 Novo okno
ISSN pri članku:0030-3992
Datum objave v DKUM:10.01.2025
Število ogledov:0
Število prenosov:22
Metapodatki:XML DC-XML DC-RDF
Področja:Ostalo
:
ŠKET, Kristijan, POTOČNIK, David, BERUS, Lucijano, HERNAVS, Jernej in FICKO, Mirko, 2025, Optimizing laser cutting of stainless steel using latin hypercube sampling and neural networks. Optics and laser technology [na spletu]. 2025. Vol. 182, no. pt. B,  112220, p. 1–10. [Dostopano 9 april 2025]. DOI 10.1016/j.optlastec.2024.112220. Pridobljeno s: https://dk.um.si/IzpisGradiva.php?lang=slv&id=91520
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Gradivo je del revije

Naslov:Optics and laser technology
Skrajšan naslov:Opt. Laser Technol.
Založnik:Elsevier
ISSN:0030-3992
COBISS.SI-ID:26072576 Novo okno

Gradivo je financirano iz projekta

Financer:ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
Številka projekta:P2-0157-2020
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.

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:optimizacija laserskega rezanja, kakovost rezalne površine, nastajanje žlindre, vzorčenje latinske hiperkocke, napredne nevronske mreže


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