Fraunhofer-Institut für Produktionstechnologie IPT

Machine learning (ML) has already been used in many applications to help optimizing production
processes. ML can be used in the production of engine components to achieve higher component quality.
For this reason, the thesis aims to automatically detect anomalies based on real acquired data of a milling
process. Accurate detection of anomalies can be used to adjust process parameters, resulting in higher
accuracies and thus higher product quality.

Your tasks

  • Familiarization with the state of the art in ML-based anomaly detection
  • Implementation and benchmarking of different ML models
  • Uncertainty quantification of ML models in production
  • Evaluation and documentation of the results

What we expect from you

  • You are studying mechanical engineering, computer science, industrial engineering, or a comparable subject
  • Initial experience in the field of machine learning is an advantage
  • A high degree of initiative and team spirit
  • Very good language skills in German and English

What you can expect from us

  • Pioneering role in ML-based anomaly detection for milling engine components.
  • An excellent set of machines and equipment
  • Participation in an industrial project and in a dedicated team

Notice: You can write your project work or thesis with us immediately and in direct cooperation with
the Chair of Metrology and Quality Management. According to the guidelines of the RWTH Aachen
University, the internship is preferably to be completed at a manufacturing company and can only take
place with us in exceptional cases.

We look forward to receiving your application
Maik Frye
Fraunhofer Institute for Production Technology IPT
Steinbachstraße 17, 52074 Aachen
maik.frye@ipt.fraunhofer.de

Um sich für diesen Job zu bewerben, sende deine Unterlagen per E-Mail an maik.frye@ipt.fraunhofer.de