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Power Control during Remote Laser Welding Using a Convolutional Neural Network

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...

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Detalles Bibliográficos
Autores principales: Božič, Alex, Kos, Matjaž, Jezeršek, Matija
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7699901/
https://www.ncbi.nlm.nih.gov/pubmed/33233723
http://dx.doi.org/10.3390/s20226658
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author Božič, Alex
Kos, Matjaž
Jezeršek, Matija
author_facet Božič, Alex
Kos, Matjaž
Jezeršek, Matija
author_sort Božič, Alex
collection PubMed
description 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.
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spelling pubmed-76999012020-11-29 Power Control during Remote Laser Welding Using a Convolutional Neural Network Božič, Alex Kos, Matjaž Jezeršek, Matija Sensors (Basel) Article 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. MDPI 2020-11-20 /pmc/articles/PMC7699901/ /pubmed/33233723 http://dx.doi.org/10.3390/s20226658 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Božič, Alex
Kos, Matjaž
Jezeršek, Matija
Power Control during Remote Laser Welding Using a Convolutional Neural Network
title Power Control during Remote Laser Welding Using a Convolutional Neural Network
title_full Power Control during Remote Laser Welding Using a Convolutional Neural Network
title_fullStr Power Control during Remote Laser Welding Using a Convolutional Neural Network
title_full_unstemmed Power Control during Remote Laser Welding Using a Convolutional Neural Network
title_short Power Control during Remote Laser Welding Using a Convolutional Neural Network
title_sort power control during remote laser welding using a convolutional neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7699901/
https://www.ncbi.nlm.nih.gov/pubmed/33233723
http://dx.doi.org/10.3390/s20226658
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