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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7699901 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT bozicalex powercontrolduringremotelaserweldingusingaconvolutionalneuralnetwork AT kosmatjaz powercontrolduringremotelaserweldingusingaconvolutionalneuralnetwork AT jezersekmatija powercontrolduringremotelaserweldingusingaconvolutionalneuralnetwork |