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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 duration -
  • Field of Research Computer Science and Media
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)

2020 | 2019


Alexander Gerling, Ulf Schreier, Andreas Hess, Alaa Saleh, Holger Ziekow, Djaffar AbdeslamA Reference Process Model for Machine Learning Aided Production Quality Management


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

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