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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 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

We look forward to receiving your application
Gustavo Laydner de Melo Rosa, Eng. Mec.
Fraunhofer Institute for Production Technology IPT
Steinbachstraße 17, 52074 Aachen
gustavo.laydner.de.melo.rosa@ipt.fraunhofer.de

Um sich für diesen Job zu bewerben, sende deine Unterlagen per E-Mail an gustavo.laydner.de.melo.rosa@ipt.fraunhofer.de