Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Autoren

  • Christian Nowroth
  • Tiansheng Gu
  • Jan Grajczak
  • Sarah Nothdurft
  • Jens Twiefel
  • Jörg Hermsdorf
  • Stefan Kaierle
  • Jörg Wallaschek

Organisationseinheiten

Externe Organisationen

  • Laser Zentrum Hannover e.V. (LZH)
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Aufsatznummer4645
FachzeitschriftApplied Sciences (Switzerland)
Jahrgang12
Ausgabenummer9
PublikationsstatusVeröffentlicht - 5 Mai 2022

Abstract

Laser beam welding is used in many areas of industry and research. There are many strategies and approaches to further improve the weld seam properties in laser beam welding. Metallography is often needed to evaluate welded seams. Typically, the images are examined and evaluated by experts. The evaluation process qualitatively provides the properties of the welds. Particularly in times when artificial intelligence is being used more and more in processes, the quantization of properties that could previously only be determined qualitatively is gaining importance. In this contribution, we propose to use deep learning to perform semantic segmentation of micrographs of complex weld areas to achieve the automatic detection and quantization of weld seam properties. A semantic segmentation dataset is created containing 282 labeled images. The training process is performed with DeepLabv3+. The trained model achieves a value of around 95% for weld contour detection and 76.88% of mean intersection over union (mIoU).

ASJC Scopus Sachgebiete

Zitieren

Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products. / Nowroth, Christian; Gu, Tiansheng; Grajczak, Jan et al.
in: Applied Sciences (Switzerland), Jahrgang 12, Nr. 9, 4645, 05.05.2022.

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Nowroth, C, Gu, T, Grajczak, J, Nothdurft, S, Twiefel, J, Hermsdorf, J, Kaierle, S & Wallaschek, J 2022, 'Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products', Applied Sciences (Switzerland), Jg. 12, Nr. 9, 4645. https://doi.org/10.3390/app12094645
Nowroth, C., Gu, T., Grajczak, J., Nothdurft, S., Twiefel, J., Hermsdorf, J., Kaierle, S., & Wallaschek, J. (2022). Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products. Applied Sciences (Switzerland), 12(9), Artikel 4645. https://doi.org/10.3390/app12094645
Nowroth C, Gu T, Grajczak J, Nothdurft S, Twiefel J, Hermsdorf J et al. Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products. Applied Sciences (Switzerland). 2022 Mai 5;12(9):4645. doi: 10.3390/app12094645
Nowroth, Christian ; Gu, Tiansheng ; Grajczak, Jan et al. / Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products. in: Applied Sciences (Switzerland). 2022 ; Jahrgang 12, Nr. 9.
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title = "Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products",
abstract = "Laser beam welding is used in many areas of industry and research. There are many strategies and approaches to further improve the weld seam properties in laser beam welding. Metallography is often needed to evaluate welded seams. Typically, the images are examined and evaluated by experts. The evaluation process qualitatively provides the properties of the welds. Particularly in times when artificial intelligence is being used more and more in processes, the quantization of properties that could previously only be determined qualitatively is gaining importance. In this contribution, we propose to use deep learning to perform semantic segmentation of micrographs of complex weld areas to achieve the automatic detection and quantization of weld seam properties. A semantic segmentation dataset is created containing 282 labeled images. The training process is performed with DeepLabv3+. The trained model achieves a value of around 95% for weld contour detection and 76.88% of mean intersection over union (mIoU).",
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AU - Nowroth, Christian

AU - Gu, Tiansheng

AU - Grajczak, Jan

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AU - Twiefel, Jens

AU - Hermsdorf, Jörg

AU - Kaierle, Stefan

AU - Wallaschek, Jörg

N1 - Funding Information: Funding: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)— CRC 1153, subproject A3—252662854. The authors would like to thank them for the support.

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