B.Sc. Thesis on Inter-Case Process Predictions

Monday, Jul 13, 2020

Motivation

Production processes often contain an amount of uncertainty caused either by human errors, the interactions between the products in the pipeline, or the complexities of creating the individual products. Moreover, the process might change from time to time by introducing different products to the pipeline at different times. It is always beneficial to evaluate the performance of the process given these changes.

Goals

  • Research, investigate, implement, and evaluate an approach for predicting quality metrics for production processes. The implemented approach should exploit the relations between the different products as well as the different attributes of individual products.

What we expect:

  • Knowledge of Python
  • Basic knowledge of Machine learning
  • Basic knowledge of deep-learning frameworks (e.g., Keras, tensorflow)
  • Version control tools (git)
  • Ability and willingness to learn

Further Information

If you have any questions or need more information, do not hesitate to ask. If you are interested in this topic, send your CV and transcript to Mohamed Behery (behery@kbsg.rwth-aachen.de).