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Power Control during Remote Laser Welding Using a Convolutional Neural Network

Laboratory for Laser Techniques, Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva cesta 6, 1000 Ljubljana, Slovenia
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Sensors 2020, 20(22), 6658; https://doi.org/10.3390/s20226658
Received: 6 October 2020 / Revised: 16 November 2020 / Accepted: 18 November 2020 / Published: 20 November 2020
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: convolutional neural network; remote laser welding; laser-power control; triangulation feedback convolutional neural network; remote laser welding; laser-power control; triangulation feedback
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Božič, A.; Kos, M.; Jezeršek, M. Power Control during Remote Laser Welding Using a Convolutional Neural Network. Sensors 2020, 20, 6658.

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