Artificial Intelligence in Manufacturing: Application of Cutting-Edge Machine Learning and Deep Learning Techniques for Optimizing Tomorrow’s Electric Drives Production (Bachelor, Project, or Master Thesis; especially for Computational Engineering and Mechatronics)

Image sources: Siemens, Code spaces


Recent trends like natural language processing, autonomous driving, service robotics or Industry 4.0 (I4.0) are mainly based on the tremendous progress made in the field of artificial intelligence (AI). Above all, machine learning (ML) techniques are of utmost importance, allowing computers to learn from data without being explicitly programmed. The increased data availability coupled with affordable computing power and extensive software solutions has laid the foundation for using such algorithms in a wide range of industrial applications, e.g. for predictive maintenance, quality prediction, machine vision, or process control.

Being confronted with increasing requirements due to electric mobility and continuing industrial automation, efficient, flexible, and reliable manufacturing processes in electric drives production are more important than ever. As classical process improvement methods such as Six Sigma are increasingly reaching their limits, ML is seen as the means to meet the ever-rising requirements in terms of time, costs, and quality. Therefore, the intelligent analysis of material, process, and quality data promises great potential for tomorrow’s electric drives production.

Possible thesis:

So far, only a few studies have dealt with the potentials of ML in electric drives production, indicating first useful application scenarios. Therefore, within the scope of this thesis, the application of cutting-edge ML, in particular DL techniques shall be evaluated using various datasets from electric drives production. The thesis is roughly divided as follows:

  • Familiarization with the relevant ML/DL depending on the main focus agreed upon (e.g., CNN, Autoencoder, GAN, Transformers, Self-supervised learning)
  • Analysis of the provided dataset(s) from electric drives production comprising tabular, time series and/or image data
  • Problem definition (e.g. visual inspection, predictive maintenance, predictive quality, process monitoring, process control) and brief literature research of related approaches from the scientific community and industrial practice, which one can orientate oneself
  • Implementation and evaluation of suitable cutting-edge ML/DL approach, following the established CRISP-DM procedure model using Python and state-of-the-art packages like PyTorch, Keras, TensorFlow, scikit-learn etc.:
    • Data understanding and pre-processing
    • Building, optimizing, and evaluating of different models
  • Discussion and outlook on further development possibilities

The exact tasks will be worked out in a joint discussion depending on prior knowledge, personal interest, and the kind of thesis (bachelor/project/master). This thesis is particularly suitable for students with a high affinity for IT who enjoy programming and want to explore cutting-edge machine learning/ deep learning algorithms on industrial datasets.

Hints and application:

  • Thesis can be worked on completely from home office, if desired
  • Remote access to a high-performance AI computer with GPU can be provided
  • Virtual regular meetings in German or English, as desired
  • Particularly suitable for IT-oriented master’s students in mechatronics, computational engineering, electrical engineering, or mechanical engineering
  • Please send questions and/or applications with resume and transcript of records via e-mail (


Andreas Mayr, M.Sc., M.Sc.




Art der Arbeit:

Bachelorarbeit, Diplomarbeit, Hauptseminar, Masterarbeit, Projektarbeit, Studienarbeit


Energietechnik, Informatik, IPEM, Maschinenbau, Mechatronik, Medizintechnik, Wirtschaftsingenieurwesen


Additive Fertigung, Fertigungsregelung und Intralogistik, Handhabungstechnik, Künstliche Intelligenz und Maschinelles Lernen, Medizintechnik, Planung und Simulation, Software Engineering und Deployment, Innovatives Qualitätsmanagement