Machine Learning in Forming Technology
Tutorial
Assistants
Christian Kubik, M. Sc.
Dirk Alexander Molitor, M. Sc.
Credit Points 4
Objective
  • Teaching basic algorithms for evaluating process states in a real production environment using a methodical machine learning approach
  • Analysis of large amounts of data using common software (Python or Matlab)
Procedure Week 1 teaches the basics of machine learning for the analysis of time series and the use of programming languages. Afterwards, in weeks 2 and 3, a machine learning approach for evaluating a forming process on the basis of sensorial acquired data is implemented in practice.
Dates
  • Expected start 20.09.2021
  • Registration in waiting list by E-Mail to with the subject “Anmeldung Tutorium ML in der UT”
  • The tutorial takes place full-time and takes 3 weeks
Number of participants
  • 2 groups with 4–6 persons
  • Single and team registration possible
Requirements
  • Participation in the lecture “Machine Learning Applications” is recommended.
  • Basic knowledge in Machine Learning and a programming language like Python or Matlab