Bachelor-/ Master Thesis: »Machine Learning for identifying optimal process parameters in manufacturing«

Webseite Fraunhofer Institute for Production Technology IPT

The Fraunhofer-Gesellschaft ( currently operates 76 institutes and research institutions throughout Germany and is the world’s leading applied research organization. Around 30,000 employees work with an annual research budget of 2.9 billion euros.

Machine Learning (ML) algorithms can recognize patterns in high-dimensional data and model complex input-output relationships. In manufacturing processes, ML models are trained on process data to obtain a mathematical representation of the production process. In this thesis, based on the »Design of Experiments« (DoE) procedure model, you will investigate how ML models can be used to identify optimal process parameters and thereby improve the underlying production process in terms of performance and efficiency.

What you will do

  • Literature search on design of experiments and optimization methods of black-box models
  • Conceptualization of experiments for benchmarking different optimization methods
  • Providing the proof of concept by conducting and evaluating the experiments
  • Derivation of a general procedure model
  • Validation of the procedure model on a production data set
  • Documentation of the results and writing your scientific paper

What you bring to the table

  • You are studying automation technology, mechanical engineering, computer science or a comparable subject
  • You are familiar with the theory and approaches of data analysis and machine learning
  • You have experience with the Python programming language and in implementing data analysis and ML pipelines
  • You are structured in your work and have good abstraction skills
  • A high degree of independence and initiative
  • Good language skills in German and/or English

What you can expect

  • Application-oriented research in linking ML and data analysis methods with industrial manufacturing processes
  • Ideal conditions for practical experience alongside your studies
  • Professional supervision and collaboration in a dedicated team
  • A state-of-the-art machine park equipped with edge cloud systems and 5G infrastructure
  • Flexible working to combine study and job in the best possible way

Questions according to this position will be answered by:
Lars Leyendecker M.Sc.
Research assistant production quality
Phone: +49 241 8904-314

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