PREFERML
Proaktive Fehlervermeidung in der Produktion durch Maschinelles Lernen
Ziel des Projektes ist es, unter Zuhilfenahme von Methoden aus dem Bereich des „Maschinellen Lernens“ Produktionsfehler zu vermeiden. Dabei gilt es insbesondere den Automatisierungsgrad bei der Erstellung und Pflege entsprechender Modelle zu erhöhen, um den operativen Einsatz praktikabel zu gestalten.
Durch die Finanzierung des Bundesministeriums können zwei Stellen für die wissenschaftliche Arbeit an dem Projekt geschaffen werden.
Projektpartner
Hochschule Furtwangen
Sick AG
Förderung
Das Projekt wird im Rahmen der Förderlinie FHprofUnt im Programm Forschung an Fachhochschulen vom Bundesministerium für Bildung und Forschung (BMBF) gefördert. Förderkennzeichen: 13FH249PX6
Publikationen
Fehlermeldung in der Produktion durch Maschinelles Lernen (Schreier)
2022 | |||
| Alexander Gerling, Holger Ziekow, Andreas Hess, Ulf Schreier, Christian Seiffer, Djaffar Ould-Abdeslam | Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric | Journal of Intelligent Manufacturing, 33.2022(2), pp. 555-573, 2022 | BibTeX | RIS DOIURN |
| Alexander Gerling, Oliver Kamper, Christian Seiffer, Holger Ziekow, Ulf Schreier, Andreas Hess, Djaffar Ould-Abdeslam | Results from using an Automl Tool for Error Analysis in Manufacturing | Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) : Volume 1, pp. 100-111, 2022 | BibTeX | RIS 978-989-758-569-2, DOI |
2021 | |||
| Christian Seiffer, Alexander Gerling, Ulf Schreier, Holger Ziekow | A Reference Process and Domain Model for Machine Learning Based Production Fault Analysis | Enterprise Information Systems : 22nd International Conference, ICEIS 2020, Virtual Event, May 5–7, 2020, Revised Selected Papers, pp. 140-157, 2021 | BibTeX | RIS 978-3-030-75418-1, DOI |
| Christian Seiffer, Holger Ziekow, Ulf Schreier, Alexander Gerling | Detection of Concept Drift in Manufacturing Data with SHAP Values to Improve Error Prediction | DATA ANALYTICS 2021 : The Tenth International Conference on Data Analytics, October 3 - 7, 2021, Barcelona, Spain, pp. 51-60, 2021 | BibTeX | RIS 978-1-61208-891-4, |
| Alexander Gerling, Holger Ziekow, Ulf Schreier, Christian Seiffer, Andreas Hess, Djaffar Ould-Abdeslam | Evaluation of Filter Methods for Feature Selection by Using Real Manufacturing Data | DATA ANALYTICS 2021 : The Tenth International Conference on Data Analytics, October 3 - 7, 2021, Barcelona, Spain, pp. 82-91, 2021 | BibTeX | RIS 978-1-61208-891-4, |
| Alexander Gerling, Christian Seiffer, Holger Ziekow, Ulf Schreier, Andreas Hess, Djaffar Ould-Abdeslam | Evaluation of Visualization Concepts for Explainable Machine Learning Methods in the Context of Manufacturing | Proceedings of the 5th International Conference on Computer-Human Interaction Research and Applications - CHIRA, 28-29 October 2021, pp. 189-201, 2021 | BibTeX | RIS 978-989-758-538-8, |
| Holger Ziekow, Ulf Schreier, Alexander Gerling, Alaa Saleh | Interpretable Machine Learning for Quality Engineering in Manufacturing - Importance Measures that Reveal Insights on Errors | The Upper-Rhine Artificial Intelligence Symposium, UR-AI 2021, Artificial Intelligence - Application in Life Sciences and Beyond, 27th October 2021, Kaiserslautern, Germany, pp. 96-105, 2021 | BibTeX | RIS DOI |
2020 | |||
| Alexander Gerling, Ulf Schreier, Andreas Hess, Alaa Saleh, Holger Ziekow, Djaffar Ould-Abdeslam | A Reference Process Model for Machine Learning Aided Production Quality Management | Proceedings of the 22nd International Conference on Enterprise Information Systems, May 5-7,2020 : Volume 1, pp. 515-523, 2020 | BibTeX | RIS 978-989-758-423-7, DOI |
| Yannick Wilhelm, Ulf Schreier, Peter Reimann, Bernhard Mitschang, Holger Ziekow | Data Science Approaches to Quality Control in Manufacturing: A Review of Problems, Challenges and Architecture | Service-Oriented Computing : 14th Symposium and Summer School on Service-Oriented Computing, SummerSOC 2020, Crete, Greece, September 13-19, 2020, pp. 45-65, 2020 | BibTeX | RIS 978-3-030-64846-6, DOI |
| Christian Seiffer, Ulf Schreier, Holger Ziekow, Alexander Gerling | KI für die Produktionsqualität | 2020 | BibTeX | RIS |
| Alexander Gerling, Alaa Saleh, Ulf Schreier, Holger Ziekow | Supporting Quality Assessment in Manufacturing by Machine Learning : First Results of PREFERML Project | Artificial Intelligence - Research Impact on Key Industries : The Upper-Rhine Artificial Intelligence Symposium UR-AI 2020; Collection of Accepted Papers of the Canceled Symposium Karlsruhe, 13th May 2020, pp. 52-53, 2020 | BibTeX | RIS 978-3-943301-29-8, |
2019 | |||
| 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 978-3-9820756-0-0, |
| 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 URN |