Supervisors: Univ.-Prof. Dr Elmar Rückert,
Vedant Dave, M.Sc.
Dr. Christoph Sorger (Dr. Christoph Sorger)
Dr. Perscha Roman (MSC Software GmbH)
Univ.-Prof. Martin Stockinger (Chair of Metal Forming)
Start date: ASAP from June 2022
Theoretical difficulty: low
Practical difficulty: low
In this thesis, the student has the unique opportunity to investigate supervised machine learning methods for predicting yield strengths using probabilistic regression models and deep learning approaches. The thesis is implemented with support of the MSC Software GmbH and the Stahl- und Walzwerk Marienhütte GmbH in Graz.
In the image above and below you see the production line at the Stahl- und Walzwerk Marienhütte GmbH in Graz.
To ensure the high quality standards, frequent ‘yield strength’ measurements are performed. These measurements have resulted in a large dataset which can now be analyzed and used to learn a prediction model. First tests were promising and the thesis will be very likely a big success.
The goal of this thesis is to analyze the data and to learn prediction models taking uncertainty estimates into account.
The models will be implemented and tested in Python.
Tentative Work Plan
To achieve our aim, the following concrete tasks will be focused on:
- Literature research on the underlying physical & chemical processes.
- Data analysis, filtering, preprocessing, visualization of the existing data.
- Implementation of deep neural networks (Variational Autoencoder), neural processes and GPs in Python. Baseline implementations are existing.
- Visualization and analysis of the prediction performance. An outlier detection and warning system should be implemented.
- (Optional) Implementation of neural time-series models like LSTMs.
- Analysis and evaluation of the provided data.