Camera-based closed-loop control for beam positioning during deep penetration welding by means of keyhole front morphology - Dilger, Pablo; Eschner, Eric; Schmidt, Michael (Procedia CIRP, 2020)
Within this paper, we present a closed-loop control system for beam positioning during laser deep penetration welding of sheet metals. The system is comprised of a high-speed camera arranged coaxially to the laser beam on a welding head and linear stages for positioning of the specimen. It is shown that the keyhole front morphology correlates with the position of the seam in butt joint configuration. Within our closed-loop control prototype, this correlation is extracted by an image processing algorithm in a real-time environment. First experiments show that our system is able to adaptively correct the beam position with sufficient accurac
Keywords: laser-based material processing; deep penetration welding; process control; image processing

Determination of the beam position in laser deep penetration welding using coaxially acquired images of the keyhole front and machine learning - Dilger, Pablo; Forster, Carola; Klein, Elias; Burger, Silvana; Eschner, Eric; Schmidt, Michael (Proceedings of the Lasers in Manufacturing (LiM) 2021, 2021)
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 syste
Keywords: laser deep penetration welding; machine learning; image processing; quality assurance

Observation of the melt dynamics during laser remote fusion cutting - Burger, Silvana; Dilger, Pablo; Schwarzkopf, Karen; Staudt, Tobias; Schmidt, Michael (Procedia CIRP, 2022)
Laser remote fusion cutting (RFC) offers a method for laser fusion cutting of metal sheets without need for a cutting gas. It is closely related to laser keyhole welding, which can transition into RFC for suitable parameters, such that the pressures in the process zone lead to a melt ejection on the bottom of the metal sheet. The mechanisms leading up to the melt expulsion are not fully understood yet and need further investigation. In this work, the cutting front is observed through a borosilicate glass sheet using a high-speed imaging system. In the setup, the glass sheet and a sample metal sheet are clamped together and the process zone is set directly at the interface between metal and glass. This setup allows for the observation of the melt dynamics adjacent to the glass sheet. In the experiments, the keyhole formation and the cutting front are investigated using a 6 kW disk laser and different feed velocities for cutting.<br

Quantification of Melt Dynamics from High-Speed Observations Within the Remote Fusion Cutting Regime by Means of Image Processing - Burger, Silvana; Dilger, Pablo; Eschner, Eric; Schmidt, Michael (2021)
Keywords: Laser cutting; Remote fusion cutting; Image processing