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
Fehlermeldung in der Produktion durch Maschinelles Lernen (Schreier)
|Holger Ziekow, Ulf Schreier, Alaa Saleh, Christof Rudolph, Katharina Ketterer, Daniel Grozinger, Alexander Gerling||Proactive Error Prevention in Manufacturing Based on an Adaptable Machine Learning Environment||Artificial Intelligence: From Research to Application: The Upper-Rhine Artificial Intelligence Symposium UR-AI 2019, March 13th, 2019, Offenburg, Germany, pp. 113-117, 2019|| |
BibTeX | RIS
|Holger Ziekow, Ulf Schreier, Alexander Gerling, Alaa Saleh||Technical Report: Interpretable Machine Learning for Quality Engineering in Manufacturing - Importance measures that reveal insights on errors||2019|| |
BibTeX | RIS