In the EmSim project, you will work with us to analyse failure-prone milling processes. The aim is to identify and classify patterns on the basis of spindle current data. The aim of your work is to develop a decision logic to optimise monitoring limits. The analysis and modeling will be carried out using Python.

Your tasks:

• analysis of existing data sets from machining tests in Python
• development of a decision logic for the adjustment of monitoring limits
• verification of the developed logic

Desirable skills and knowledge:

• interest in machining technologies and data analysis
• initial experience with machine learning methods or interest in familiarising yourself with them
• good programming skills in C# or Python

AI-based prediction of component quality enables cost-effective 100 % quality control in manufacturing. In this study a real production dataset has to be analyzed and different AI models evaluated. The goal is to calculate quality parameters with high accuracy based on features in process signals. The focus of the work is on sequence-based AI approaches, which are particularly suitable for processing time series.

Your tasks:

• programming Python scripts for data analysis and preprocessing
• training and evaluating AI models

Desirable skills and knowledge:

• proficiency in German or English
• independent and structured working style
• strong knowledge in time series analysis with Python