Conference Agenda

Macro: System Technology and Process Control 2
Tuesday, 22/June/2021:
2:45pm - 4:00pm

Session Chair: Jonas Wagner, Universität Stuttgart, Germany
Location: Room 4
ICM 1st Floor 433

2:45pm - 3:00pm

Active mirrors for plan field correction in laser material processing

Paul Böttner1, Aoife Brady1, Claudia Reinlein3,2, Ramona Eberhardt1, Stefan Nolte1,2

1Fraunhofer IOF, Germany; 2FSU Jena, Germany; 3Robust AO GmbH, Germany

This paper reports on an approach to increase the scan field and the dynamic range of post objective scanners. An active mirror in combination with a fixed focusing lens is used to adjust the optimal focus length depending on the beam position in the scan field.

The active mirror has an adjustment range from infinity to 0.5 dpt with a step response time of 2 ms. The scan field is determined by the focal length of the focusing lens. When using a focal length of 250 mm, a scan field of (100 x 100) mm² is achieved. Doubling the focal length increases the scan field to (500 x 500) mm². Measurements with a raw beam diameter of 20 mm and a wavelength of 1064 nm provide a spot diameter of 34 µm with a focal length of 250 mm and 75 µm with a focal length of 500 mm.

3:00pm - 3:15pm

Fixture-free laser-beam-welding

Georgij Safronov1, Alexander Grimm1, Florian Schlather1, Markus Puschmann2, Philipp Ronald Engelhardt1, Markus Lachenmaier1

1BMW AG, Germany; 2Fraunhofer Institute for Machine Tools and Forming Technology, Germany

Today’s automotive Bodyshop is dominated by resistance-point-welding due to low cost and robustness, especially regarding part-quality-variations. In contrast laser-beam-welding is struggling to create a solid business case in big numbers even so it has technological advancements like the welding-speed. To economically resolve this competition, we need an approach to create a balanced synergy between both joining methods. BMW together with the Fraunhofer Society (IWU) and the Technical University Munich (iwb) approached this challenge by developing a method to combine both welding procedures through a lap-joint-flange integrated geometry, which can be brought in off-tool in the press-shop.

These functional geometries allow us to use the advantages of resistance-point-welding to fix the geometry and create a laser-suitable gap-situation without clamping-tools for the following laser-welding. We also proved the technological and the financial viability of this method and we believe this could be a game-changer for laser-welding in the automotive industry.

3:15pm - 3:30pm

Remote laser welding system with automatic 3D teaching, in-line 3D seam tracking, and adaptive power control

Matija Jezersek1, Matjaž Kos1, Erih Arko2, Hubert Kosler2

1University of Ljubljana, Faculty of Mechanical Engineering, Laboratory for Laser Techniques, Aškerčeva cesta 6, 1000 Ljubljana, Slovenia; 2Yaskawa Slovenija d.o.o., Lepovče 23, 1310, Ribnica, Slovenia

An adaptive remote laser welding system, based on triangulation feedback control is presented. It enables off-line measuring of a workpiece 3D shape, in-line 3D seam tracking, and in-line laser power control, which are extremely important features for producing sound welds on complex geometries. The 3D measuring is done by a triangulation camera and the laser’s pilot beam. The same camera is utilized to determine the 3D seam position and to monitor the key process features, including the weld penetration depth.

Results show high 3D measuring precision in the lateral (0.05 mm) and vertical (0.3 mm) direction. Additionally, laser power control significantly reduces penetration depth and plasma oscillations. Thus, adaptive laser welding can be used for small series and customized production of parts where a highly flexible, precise, and cost-effective joining technology is required.

3:30pm - 3:45pm

Image-based Real-Time Defect Detection during Laser Welding using Ensemble Deep Learning on Low Power Embedded Computing Boards

Christian Knaak, Jakob von Eßen, Peter Abels, Arnold Gillner

Fraunhofer ILT, Germany

Advanced and intelligent process monitoring strategies are required to enable an unambiguous diagnosis of the process situation and thus of the final component quality. Additionally, the ability to recognize the current process situation in terms of quality is also a key requirement for autonomous manufacturing systems. To address these needs, this study investigates a novel ensemble deep learning architecture based on convolutional neural networks (CNN), gated re-current units (GRU) combined with high performance classification algorithms such as k-nearest neighbors (kNN) and support vector machines (SVM). The architecture uses spatio-temporal features extracted from infrared image sequences to locate critical welding defects including lack of fusion (false friends), sagging and lack of penetration, and geometric deviations of the weld seam. In order to evaluate the proposed architecture, this study investigates a comprehensive scheme based on classical machine learning methods using manual feature extraction and state of the art deep learning algorithms. Optimal hyperparameters for each algorithm are determined by an extensive grid search.

3:45pm - 4:00pm

System design for reliable and robust laser-welding of copper in automotive series production

Stefan Mücke, Pravin Sievi, Steffen Walter, Florian Albert

Scansonic MI GmbH, Germany

The evolution of mobility away from ICEs towards electric or electrified drives also created some new challenges for the series production of drive train components. With copper, a new material moves into focus in the drive train which needs to be welded. No matter if e-motors, e-boxes or batteries, copper needs to be welded and the laser fits best for the requests in most cases. The presentation will focus on what has to be considered for welding copper and to face these issues with an intelligent system-design to fit series production needs. Beside the design of the welding system with high-speed scanners and arrangement of the necessary components, the presentation also will focus on the matter of detecting the welding areas correctly to create a robust process that reduces costs and can cope with series production conditions.