MACH4.0 – Application of Data Analytics in machining production​

Course​ Tutorium (Master)​
Date​ The tutorial starts on 02.11.2020 until 31.01.2021.

We offer a consultation hour on Thursdays from 15:00 – 17:00. The 1st appointment is on Thursday 05.11. In November we will discuss the basics of machining and the analysis of machine data with Python. In December we will start the project in teamwork until the end of January.

Dates for the oral exam will be arranged with the groups.
Registration​ Due to a limited number of participants, registration in TUCaN must first be clarified with Oliver Kohn M. Sc..
Note​ Dear students,​

since the current situation means that face-to-face events should be avoided, the content is provided in a digital form. During the regular consultation hours, content can be discussed and questions or problems clarified when required.​

Best regards​
Oliver Kohn​
Intention 1. Explain basic concepts for data acquisition, processing and storage for machine tools in the context of Industry 4.0.​
2. Using domain knowledge from the field of machining, to identify and evaluate relevant production data regarding various technical problems.​
3. Process and visually prepare time series data from production using Python.​
4. Critically evaluate methods from the field of data science for an analysis of production data and implement them with Python.​
5. Develop and validate solutions for technical problems in a team.​
6. Prepare a visual presentation of the results.
Aims The aim is to teach and implement modern methods from the field of data science for the evaluation of production data from machine tools. One use case is the detection of process anomalies. As a basis the requirements for automation and connection of machine tools in the context of Industry 4.0 will be dealt with. For a deeper understanding of the cutting processe, domain knowledge from the field of machining is also addressed.​
Activities​ Part 1: Basics​
​Process understanding for the application in machining production.​
Practical examples in python.​

Part 2: Data Challenge as final project​
​Analysis of production data in teams.​
Documentation of the results.
Contact Oliver Kohn M. Sc.
Alexander Fertig M. Sc.