Masterarbeit: Intelligent Contact Preconditioning for Superior Wear Performance of Rolling/Sliding Contacts: Experimental and Modelling Insights (Tribology, Boundary layer, Wear, Machine learning)

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/sliding 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 such contacts is the formation and behaviour of boundary layers. These thin films, which develop at the interface between the contacting surfaces, play a pivotal role in minimizing wear and enhancing the overall efficiency and durability of the systems. This thesis vacancy offers an exciting opportunity to contribute towards simplifying the complex relationship between contact preconditioning (which leads to different kinds of boundary layers) and wear in rolling/sliding contacts.

Tasks:

• Analyzing test specimens from rolling/sliding contact experiments using various surface characterization techniques and building an understanding
• Developing/improvising a supervised machine learning model to determine the cause-effect relationships between contact preconditioning and wear performance
• Literature review on the wear behavior of rolling/sliding contacts, wear mechanisms, and boundary layers

Requirements:

• Independent working
• Critical thinking
• Basic knowledge of machine learning
• Knowledge of material/surface characterization is an advantage
• Background in scientific 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
ankit.saxena@imse.rwth-aachen.de