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M.Sc. Tobias Schlagenhauf

Research Associate
department: Machines, Equipment and Process Automation
office hours: to be agreed
room: 129, Geb. 50.36
phone: +49 1523 9502610
tobias schlagenhaufRvs0∂kit edu

76131 Karlsruhe
Kaiserstraße 12


M.Sc. Tobias Schlagenhauf

Forschungs- und Arbeitsgebiete:

  • Machines, Equipment and Process Automation under the use of machine learning techniques
  • Data Science in the industrial context
  • Research area of the fourth industrial revolution

 

General task:

  • Lecture supervisor: Umformtechnik

 

Curriculum Vitae:

since 08/2018 Research Associate at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT)

 

Publications

[ 1 ] Schlagenhauf, T.; Hillenbrand, J.; Klee, B. & Fleischer, J. (2019), "Integration von Machine Vision in Kugelgewindespindeln", wt Werkstattstechnik online, pp. 605-610.
Abstract:
Unvorhergesehene Maschinenausfälle von Werkzeugmaschinen durch natürlichen Verschleiß sind häufig auf den Kugelgewindetrieb zurückzuführen. Für eine frühzeitige Erkennung der auftretenden Schäden, präsentiert dieser Beitrag einen Ansatz für die Überwachung von Spindeln von Kugelgewindetrieben mittels integriertem Kamera - system. Ziel ist die frühzeitige Detektion von Schäden, die auf der Spindeloberfläche erscheinen, um entsprechende Wartungsmaßnahmen abzuleiten.

[ 2 ] Schlagenhauf, T.; Feuring, C.; Hillenbrand, J. & Fleischer, J. (2019), "Camera Based Ball Screw Spindle Defect Classification System". Production at the leading edge of technology, eds. Springer, pp. 503-512.
Abstract:
This paper shows how to detect Pitting on a ball screw drive (BSD) with the help of a Convolutional Neural Network (CNN). Building on a previous approach where we presented an integrated camera system for the BSD by applying a camera to the nut which is able to monitor the ball screw spindle´s surface, this paper deals with the Condition Monitoring of the component by feeding spindle surface images into a CNN to identify Pitting defects. The authors develop a CNN that is able to distinguish between images showing Pitting and images without. For training purposes a balanced dataset of images with and without Pitting is used. With a number of 200 images, a four-fold Cross Validation approach is used to maximize the amount of data used for training and testing. The trained model performs with a mean accuracy of 91,50% on the new test data. Further, the model performs with a mean precision of 93,68% at a recall of 89,00%.