Bachelor- / Master’s thesis: Machine Learning Based Optimization of Boundary Layer Formation for Enhanced Wear Protection in Rolling Contacts (Tribology, Boundary layer, Wear protection, Tribo-informatics)

Webseite Institut für Maschinenelemente und Systementwicklung

The Institute of Machine Elements and System Engineering researches the fundamental structural and tribological behaviour of machine elements and represents them in experimentally validated model descriptions. These model descriptions are used to analyse and design the functional, loss and noise behaviour of entire technical systems with a focus on drive technology. The developed models are also used to research and develop methods of Model Based Systems Engineering as a central element of future inductive product development processes.
Rolling contacts, such as those found in bearings, gears, and other machine elements are integral components of numerous mechanical systems. The efficient operation and longevity of these systems depend on the intricate interplay between the contacting surfaces and the lubricant. One critical aspect that significantly influences the performance of rolling contacts is the formation and behaviour of tribolayers. These thin films, which develop at the interface between the contacting surfaces, play a pivotal role in reducing friction, minimizing wear, and enhancing the overall efficiency and durability of the systems. This thesis vacancy offers an exciting opportunity to contribute to the improvement of wear protection in rolling contacts by optimizing the boundary layer formation. The project aims to investigate the influence of boundary layer formation on wear protection in rolling contacts.


  • Conducting a comprehensive literature review on the wear behavior of rolling contacts, boundary layer formation, and wear protection mechanisms
  • Analyzing experimental data from rolling contact experiments to identify correlations between initial boundary layer properties, contact parameters, and wear behavior of rolling bearings
  • Applying machine learning models to determine the cause-effect relationships between the above mentioned features


  • Proficiency in machine learning
  • Critical thinking
  • Independent working
  • Background knowledge in tribology, experimental methods, rolling contact behavior, and material characterization
  • Previous experience in research writing is an advantage

We offer:

  • Intensive support and supervision
  • Excellent working atmosphere
  • Suitability for homeoffice
  • Immediate start or by appointment
  • Promising topic and experience for a future career
  • A warm welcome to new ideas!


We look forward to your application by email:

Ankit Saxena, Ph. D.
Institute for Machine Elements and Systems Engineering

Schinkelstraße 10, 52062 Aachen


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