Power control during remote laser welding using a convolutional neural network
ID Božič, Alex (Author), ID Kos, Matjaž (Author), ID Jezeršek, Matija (Author)

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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.

Keywords:convolutional neural network, remote laser welding, laser-power control, triangulation feedback
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Publication status:Published
Publication version:Version of Record
Number of pages:15 str.
Numbering:Vol. 20, iss. 22, art. 6658
PID:20.500.12556/RUL-127284 This link opens in a new window
ISSN on article:1424-8220
DOI:10.3390/s20226658 This link opens in a new window
COBISS.SI-ID:39691267 This link opens in a new window
Publication date in RUL:02.06.2021
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Shortened title:Sensors
COBISS.SI-ID:10176278 This link opens in a new window


License:CC BY 4.0, Creative Commons Attribution 4.0 International
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:20.11.2020

Secondary language

Keywords:konvolucijske nevronske mreže, lasersko daljinsko varjenje, nadzor moči laserja, triangulacijska povratna zanka


Funder:Other - Other funder or multiple funders
Funding programme:Slovenia
Project number:C3330-16-529000

Funder:EC - European Commission
Funding programme:ERDF

Funder:ARRS - Slovenian Research Agency
Project number:P2-0392

Funder:ARRS - Slovenian Research Agency
Project number:L2-8183
Name:Visoko prilagodljivi vlakenski laserji velikih moči za uporabo v industriji

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