Power control during remote laser welding using a convolutional neural network

Božič, Alex (avtor), Kos, Matjaž (avtor), Jezeršek, Matija (avtor)

The increase in complex workpieces with changing geometries demands advanced control algorithms in order to achieve stable welding regimes. Usually, many experiments are required to identify and confirm the correct welding parameters. We present a method for controlling laser power in a remote laser welding system with a convolutional neural network (CNN) via a PID controller, based on optical triangulation feedback. AISI 304 metal sheets with a cumulative thickness of 1.5 mm were used. A total accuracy of 94% was achieved for CNN models on the test datasets. The rise time of the controller to achieve full penetration was less than 1.0 s from the start of welding. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was used to further understand the decision making of the model. It was determined that the CNN focuses mainly on the area of the interaction zone and can act accordingly if this interaction zone changes in size. Based on additional testing, we proposed improvements to increase overall controller performance and response time by implementing a feed-forward approach at the beginning of welding.

• Jezik: Angleški jezik
• Ključne besede: convolutional neural network, remote laser welding, laser-power control, triangulation feedback
• 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: 15 str.
• Številčenje: Vol. 20, iss. 22, art. 6658
• PID: 20.500.12556/RUL-127284
• UDK: 621.791.725:004.032.26(045)
• ISSN pri članku: 1424-8220
• DOI: 10.3390/s20226658
• COBISS.SI-ID: 39691267
• Datum objave v RUL: 02.06.2021

BOŽIČ, Alex, KOS, Matjaž in JEZERŠEK, Matija, 2020, Power control during remote laser welding using a convolutional neural network. Sensors [na spletu]. 2020. Vol. 20, no. 22,  6658. [Dostopano 5 maj 2025]. DOI 10.3390/s20226658. Pridobljeno s: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=127284

Gradivo je del revije

• Naslov: Sensors
• Skrajšan naslov: Sensors
• Založnik: MDPI
• ISSN: 1424-8220
• COBISS.SI-ID: 10176278

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: 20.11.2020

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: konvolucijske nevronske mreže, lasersko daljinsko varjenje, nadzor moči laserja, triangulacijska povratna zanka