Dr. Jens Reiser Profile

Dr. Jens Reiser

at Precitec GmbH

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Publications (3)

Proceedings Article | 12 March 2024 Presentation + Paper

Meeting the challenges in e-mobility with laser welding and sophisticated system technology

Markus Kogel-Hollacher, Jens Reiser, Thomas Nicolay, Joachim Schwarz

Proceedings Volume 12873, 128730P (2024) https://doi.org/10.1117/12.3004992

KEYWORDS: Batteries, Laser welding, Laser processing, Industrial applications, Sensors, Industry, Ice, Artificial intelligence, Sensor technology, Process control

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Proceedings Article | 20 September 2023 Paper

Interferometric surface inspection with movable measurement spot: high-speed OCT imaging for thickness and topography in industry

M. Kogel-Hollacher, T. Nicolay, S. Mieth, Jens Reiser

Proceedings Volume 12607, 1260702 (2023) https://doi.org/10.1117/12.3005058

KEYWORDS: Sensors, Inspection, Scanners, Interferometry, Head, Information technology, Industry, Optical coherence tomography, Mirrors, Distance measurement

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The increasing demands in industry, for example for products in the consumer electronics sector or for assistance systems in cars, and the continuous development in semiconductor are leading to significant miniaturization in electronic components. These requirements are inevitably also transferred to ultra-precise manufacturing and thus ask for monitored production steps. In the context of Industry 4.0 and other developments in the context of modern production sensors to supervise production steps are crucial. An essential component here is non-destructive testing (NDT) and specifically optical metrology. Complete 3D measurement of objects using white light interference in mass production is time consuming and therefore not well-suited for use as a means of inline inspection. In addition, the long measurement cycles with complex sequences generate large amounts of data and the effort for processing the data must also be included in overall considerations. The Flying Spot Scanner (FSS) intelligently avoids these disadvantages and is therefore ideal for inline inspections. To meet the need of additional, even complex and time sensitive measurement tasks, Precitec Optronik has developed the Flying Spot Sensor. The active measuring head was specially developed for in-line use and ideally complements the spectral interferometric sensor to form a smart inspection system. The light from the sensor is coupled into the measuring head via a light guide and deflected by a mirror system, a so-called galvanometer scanner. Finally, the light passes through a telecentric lens, which serves as a focusing module on the outward path and as a measuring aperture for the reflected light. Due to the movable mirror system, the measuring light beam can be deflected at different angles and thus the measuring spot can be freely positioned within the field of view of the lens. Long paths of linear axes are replaced by short rotary movements, resulting in an extreme reduction of the measuring, or scanning time. By using specialize focusing modules, the Flying Spot Scanner can be adapted to different application scenarios. These optics are characterized by a low curvature of the focal plane, very small telecentric errors and a very large depth of field. The measurement system can also be used in two operating modes, a thickness mode, or a distance mode. The two operating modes can be selected at will via the digital interface, which means that the switching process can be easily integrated into an automatic measuring sequence.

Proceedings Article | 15 March 2023 Presentation + Paper

Quality and physical properties revealed: sensors for monitoring and controlling laser processes in combination with sophisticated data models

Markus Kogel-Hollacher, Jens Reiser, Sebastian Moser, Fabian Mack, Joachim Schwarz

Proceedings Volume 12414, 1241409 (2023) https://doi.org/10.1117/12.2646187

KEYWORDS: Machine learning, Batteries, Laser processing, Artificial intelligence, Data modeling, Sensors, Process control, Data processing, Signal processing, Materials properties, Sensor technology

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There are many distinct commercial sensor systems for monitoring or controlling laser processes. Most of them are based on camera or photodiode technology. Furthermore, the introduction of real in-process measuring systems in laser material processing like OCT has significantly increased the safety of both defect detection and process control [5][6][7][8][9]. The focus of this contribution, however, relates to the use of artificial intelligence algorithms to "See New Things". We will discuss how classified, physical properties can be derived from already reliable process information - "seeing the unseen", so to speak. Instead of defining complex rules for algorithms, the use of Data Science and Machine Learning methods reveals hidden structures in noisy unstructured data and make it possible to find the relationships of the data to the physical measurement. There are several systems available in the market which capture or measure and analyze physical effects of the process zone and their properties during the laser processing, but none of these effects and properties stand for “seam quality” for themselves, which is typically defined by mechanical, geometrical and metallurgical properties of the solidified seam. The process properties like emitted visible and thermal radiation or geometrical values of the melt pool or the penetration depth of the laser forming the keyhole (penetration depth) only provide indications of how the desired quality might be achieved. When welding thousands of seams per shift during serial production for Li-Ion batteries in e-mobiles, very often users would like to stop their fully automatic machines once the laser process is suddenly running out of previously set boundaries. Such an approach allows users to find systematic faults where and when they occur, rather than at the end of the line where quality inspection takes place and the line is still running, producing more and more parts with the same faults. When it comes to the use of artificial intelligence algorithms the following questions must be answered: ▪ Is it possible to map an input time series to a real value? ▪ Can we get out the strength of the weld from the process emissions? ▪ Is the information buried in the signal? The vision, which can be represented by Figure 1, is that by using the data from process emissions and specific data models, a relationship to physical quantities can be established. Thus, AI goes a significant step beyond the simple "good-bad" statement.

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