Univ. Prof. Dr. Ing.

Bergs, Thomas

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  • Publication
    Investigations on five-axis milling and subsequent five-axis grinding of gears
    ( 2022)
    Bergs, Thomas
    ;
    Schneider, Sebastian A. M.
    ;
    Janßen, Christopher
    ;
    Jahnel, Kirk
    ;
    Ganser, Philipp
    ;
    Brimmers, Jens
    High-productivity processes such as gear hobbing and gear grinding are normally used in the manufacturing of gears. In some applications, however, the use of these processes is not possible due to accessibility. One example is the production of planetary gears with double helical teeth for the gear box in modern aircraft engines. The gear boxes are used to increase the efficiency of the engines but should be as light and compact as possible. Thus, the tool runout area for gear hobbing or gear grinding tools is too small. One way of manufacturing these gears is five-axis machining. While five-axis milling of gears has been the subject of several publications, five-axis grinding of gears has hardly been a topic. This paper presents the results of investigations on five-axis milling and subsequent five-axis grinding of gears in comparison to conventionally manufactured gears. For this purpose, after hardening, gears were first five-axis milled and then five-axis ground using different process parameters and then investigated on back-to-back test rigs regarding load carrying capacity. In addition, the dimensional accuracy was measured and the surfaces were examined metallographically. The dimensional accuracy became worse after five-axis grinding. However, in terms of gear life, the five-axis milled and then five-axis ground variant showed an increase of 8.2 % compared to the conventionally manufactured gear which indicates a high potential for further research in regard to the presented five-axis machining process chain of gears.
  • Publication
    Electrochemical defect analysis (EC-D) of additive manufactured components
    ( 2021)
    Sous, Florian
    ;
    Herrig, Tim
    ;
    Bergs, Thomas
    ;
    Karges, Florian
    ;
    Feiling, Nicole
    ;
    Zeis, Markus
    Due to more freedom in design and flexibility in production, parts produced by additive manufacturing technologies (AM) offer a huge potential for the manufacture of turbomachinery components. Because of the layer by layer built structure, internal defects like cracks or gaseous pores can occur. These defects considerably reduce the mechanical properties and increase the importance of quality control, especially in the field of turbomachinery. Therefore, in this study, an electrochemical defect analysis (EC-D) of additive manufactured components is introduced, performed and validated in comparison to a nondestructive X-ray testing of the same part. A test rig was developed, which allows an alternation between electrochemical machining and subsequent optical documentation of each removed layer. The documentation of the surface and the macroscopic defects in the AM-parts are captured by an integrated camera system.
  • Publication
    Reflectometry-based investigation of temperature fields during dual-beam Laser Metal Deposition
    ( 2021)
    Gipperich, Marius
    ;
    Peters, Christian
    ;
    Riepe, Jan
    ;
    Day, Robin
    ;
    Bergs, Thomas
    Laser Metal Deposition (LMD) is a high deposition rate metal Additive Manufacturing process. Its applications are basically repair, cladding and manufacturing. The two most commonly used LMD processes are powder-based (LMD-p) and wire-based (LMD-w). Despite the fact that wire-based LMD is more material efficient, process stability is a major concern. By adding a modulated laser beam to the continuous process beam, a change of the melt pool geometry and increased energy absorption are observed. This relation shows great potential to increase process stability. In this contribution, the positive effect of the dual laser-beam use on LMD-w processes is demonstrated. To understand the cause-effect relation, the workpiece temperature field was investigated by optical backscatter reflectometry ( OBR). The results were then correlated to simultaneously performed IR camera measurements of the workpieces upper surface. By better understanding the thermal phenomena in dual-beam LMD, research can improve process temperature control. This leads to a new perspective for the LMD-w manufacturing process in many industry sectors such as mobility, energy and engineering.
  • Publication
    Pulsed Laser Influence on Temperature Distribution during Dual Beam Laser Metal Deposition
    ( 2020)
    Gipperich, Marius
    ;
    Riepe, Jan
    ;
    Arntz, Kristian
    ;
    Bergs, Thomas
    Wire-based Laser Metal Deposition (LMD-w) is a suitable manufacturing technology for a wide range of applications such as repairing, coating, or additive manufacturing. Employing a pulsed wave (pw) laser additionally to the continuous wave (cw) process laser has several positive effects on the LMD process stability. The pw-plasma has an influence on the cw-absorption and thus the temperature distribution in the workpiece. In this article, several experiments are described aiming to characterize the heat input during dual beam LMD. In the first setup, small aluminum and steel disks are heated up either by only cw or by combined cw and pw radiation. The absorbed energy is then determined by dropping the samples into water at ambient temperature and measuring the waters temperature rise. I n a second experiment, the temperature distribution in the deposition zone under real process conditions is examined by two-color pyrometer measurements. According to the results, the pw plasma leads to an increase of the effective absorption coeffcient by more than 20%. The aim of this work is to achieve a deeper understanding of the physical phenomena acting during dual beam LMD and to deploy them selectively for a better and more flexible process control.
  • Publication
    Digital image processing with deep learning for automated cutting tool wear detection
    ( 2020)
    Bergs, Thomas
    ;
    Holst, Carsten
    ;
    Gupta, Pranjul
    ;
    Thorsten Augspurger
    Tool wear is a cost driver in the metal cutting industry. Besides costs for the cutting tools themselves, further costs appear - equipment downtime for tool changes, reworking of damaged surfaces, scrap parts or damages to the machine tool itself in the worst case. Consequently, tools need to be exchanged on a regular basis or at a defined tool wear state. In order to detect and monitor the tool wear state different approaches are possible. In this publication, a deep learning approach for image processing is investigated in order to quantify the tool wear state. In a first step, a Convolutional Neural Networks (CNN) is trained for cutting tool type classification. This works well with an accuracy of 95.6% on the test dataset. Finally, a Fully Convolutional Network (FCN) for semantic segmentation is trained on individual tool type datasets (ball end mill, end mill, drills and inserts) and a mixed dataset to detect worn areas on the microscopic tool images. The accuracy metric for this kind of task, Intersect over Union (IoU), is around 0.7 for all networks on the test dataset. This paper contributes to the perspective of a fully automated cutting tool wear analysis method using machine tool integrated microscopes in the scientific and industrial environment.