Current information from HFU about the Corona virus:

Proactive Error Avoidance in Production through Machine Learning

The aim of the project is to avoid production errors using methods from the field of "machine learning". In doing so it will be focusing on increasing the level of automation in the development and maintenance of particular models in order to make operational deployment feasible.

Financing by the Federal Ministry means that 2 research positions have been created for the project.

Project partners

Furtwangen University
Sick AG


The project is funded by the Federal Ministry of Education and Research (BMBF) through the Research at Universities of Applied Sciences programme (Forschung an Fachhochschulen) within the framework of the FHprofUnt funding stream. Reference code: 13FH249PX6

PREFERML (I19933-1)
PREFERML (I19933-2)

Fehlermeldung in der Produktion durch Maschinelles Lernen (Schreier)

2021 | 2020 | 2019


Christian Seiffer, Alexander Gerling, Ulf Schreier, Holger ZiekowA Reference Process and Domain Model for Machine Learning Based Production Fault Analysis


Alexander Gerling, Ulf Schreier, Andreas Hess, Alaa Saleh, Holger Ziekow, Djaffar O. AbdeslamA Reference Process Model for Machine Learning Aided Production Quality Management
Yannick Wilhelm, Ulf Schreier, Peter Reimann, Bernhard Mitschang, Holger ZiekowData Science Approaches to Quality Control in Manufacturing: A Review of Problems, Challenges and Architecture
Christian Seiffer, Ulf Schreier, Holger Ziekow, Alexander GerlingKI für die Produktionsqualität
Alexander Gerling, Alaa Saleh, Ulf Schreier, Holger ZiekowSupporting Quality Assessment in Manufacturing by Machine Learning : First Results of PREFERML Project


Holger Ziekow, Ulf Schreier, Alaa Saleh, Christof Rudolph, Katharina Ketterer, Daniel Grozinger, Alexander GerlingProactive Error Prevention in Manufacturing Based on an Adaptable Machine Learning Environment
Holger Ziekow, Ulf Schreier, Alexander Gerling, Alaa SalehTechnical Report: Interpretable Machine Learning for Quality Engineering in Manufacturing - Importance measures that reveal insights on errors

Contact details