Dilger P, Forster C, Klein E, Burger S, Eschner E, Schmidt M (2021)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2021
Publisher: German Scientific Laser Society (WLT)
Conference Proceedings Title: Proceedings of the Lasers in Manufacturing (LiM) 2021
Event location: Online
The joining technology of laser beam welding offers high flexibility and productivity. However, the small laser beam focus demands dependable quality assurance to ensure a sufficient connection of the parts. In keyhole welding of metal sheets in butt joint configuration, a gap is visible at the keyhole front, which correlates with the leading joint position. This process feature can be used for quality control by arranging a high-speed camera coaxially to the laser beam to monitor the keyhole. Here, we present a machine learning approach for a robust determination of the beam position relative to the joint based on the keyhole front morphology. For this purpose, we conducted a series of experiments to produce a set of labeled images, which are used to train a convolutional neural network. After training on the data, the network can predict the position of the keyhole front gap, setting the foundation for a quality control system.
APA:
Dilger, P., Forster, C., Klein, E., Burger, S., Eschner, E., & Schmidt, M. (2021). Determination of the beam position in laser deep penetration welding using coaxially acquired images of the keyhole front and machine learning. In Proceedings of the Lasers in Manufacturing (LiM) 2021. Online: German Scientific Laser Society (WLT).
MLA:
Dilger, Pablo, et al. "Determination of the beam position in laser deep penetration welding using coaxially acquired images of the keyhole front and machine learning." Proceedings of the Conference on Lasers in Manufacturing (LiM), Online German Scientific Laser Society (WLT), 2021.
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