Daniel Lieber
Technical University of Dortmund
5 Papers
23 Citations
Daniel Lieber is an academic researcher from Technical University of Dortmund. The author has contributed to research in topics: Quality (business) & Process (engineering). The author has an hindex of 5, co-authored 5 publications.
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Papers
Quality Prediction in Interlinked Manufacturing Processes based on Supervised & Unsupervised Machine Learning
TL;DR: In this paper, the authors presented a methodical framework based on data mining for predicting the physical quality of intermediate products in interlinked manufacturing processes in rolling mill case study, and showed how the combination of supervised and unsupervised data mining methods can be applied to identify most striking operational patterns, promising quality-related features and production parameters.
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Striving for Zero Defect Production: Intelligent Manufacturing Control Through Data Mining in Continuous Rolling Mill Processes
Benedikt Konrad,Daniel Lieber,Jochen Deuse +2 more
- 01 Jan 2013
TL;DR: In this article, the authors presented a process control approach that provides the opportunity of gaining transparency on quality properties of intermediate products by predicting intermediate product's quality by means of data mining techniques.
22
Sustainable Interlinked Manufacturing Processes through Real-Time Quality Prediction
Daniel Lieber,Benedikt Konrad,Jochen Deuse,Marco Stolpe,Katharina Morik +4 more
- 01 Jan 2012
TL;DR: Based on a rolling mill case study, the authors discusses how data mining techniques and intelligent machine-to-machine telematics could be used to predict internal quality issues of intermediate products in manufacturing processes and discover hidden information, knowledge and dependencies in the process data contribute significantly to support avoiding waste of resources and achieving the objectives of zero-defect production, sustainable and energy-efficient manufacturing processes.
15
Challenges for Data Mining on Sensor Data of Interlinked Processes
Jochen Deuse,Benedikt Konrad,Daniel Lieber,Katharina Morik,Marco Stolpe +4 more
- 28 Feb 2012
TL;DR: Based on a rolling mill case study, the paper discusses how decentralized data mining and intelligent machine-to-machine communication could be used to predict the physical quality of intermediate products online and in real-time for detecting quality issues as early as possible.
Wissensentdeckung im industriellen Kontext
Daniel Lieber,Olga Erohin,Jochen Deuse +2 more
- 28 Jun 2013
TL;DR: Kurzfassung Die effektive Nutzung der Ressource Wissen zur Entscheidungsunterstutzung in Produktions-and Planungsprozessen ist heute fur den Unternehmenserfolg von hochster Bedeutung as mentioned in this paper.
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