Power control during remote laser welding using a convolutional neural network

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

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
• UDC: 621.791.725:004.032.26(045)
• ISSN on article: 1424-8220
• DOI: 10.3390/s20226658
• COBISS.SI-ID: 39691267
• Publication date in RUL: 02.06.2021

Record is a part of a journal

• Title: Sensors
• Shortened title: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• COBISS.SI-ID: 10176278

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