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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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Abstract
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.

Language:English
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
Year:2020
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
UDC:621.791.725:004.032.26(045)
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
Views:905
Downloads:189
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Record is a part of a journal

Title:Sensors
Shortened title:Sensors
Publisher:MDPI
ISSN:1424-8220
COBISS.SI-ID:10176278 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
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

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

Projects

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

Funder:EC - European Commission
Funding programme:ERDF
Acronym:GOSTOP

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

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

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