Ziegenbein, Amina ; Fertig, Alexander ; Metternich, Joachim ; Weigold, Matthias (2020)
Data-Based Process Analysis in Machining Production: Case Study for Quality Determination in a Drilling Process.
In: Procedia CIRP, 93
doi: 10.1016/j.procir.2020.03.063
Article, Bibliographie
Abstract
The rush of new providers of Industrial Internet of Things solutions and machine learning applications onto the market opens up new possibilities for data acquisition and analysis that go beyond the classical approach of model- and empirical-based process analysis. In this context, classic production tasks, e.g. quality assurance by random sampling, should be critically reviewed for their relevance. These non-value-adding activities can potentially be eliminated by disruptive digitalisation in order to increase labour productivity. This paper showcases the potential of a data-driven approach for quality determination in a drilling process using machine control data.
| Item Type: | Article |
|---|---|
| Erschienen: | 2020 |
| Creators: | Ziegenbein, Amina ; Fertig, Alexander ; Metternich, Joachim ; Weigold, Matthias |
| Type of entry: | Bibliographie |
| Title: | Data-Based Process Analysis in Machining Production: Case Study for Quality Determination in a Drilling Process |
| Language: | English |
| Date: | 29 September 2020 |
| Publisher: | Elsevier B.V. |
| Journal or Publication Title: | Procedia CIRP |
| Volume of the journal: | 93 |
| DOI: | 10.1016/j.procir.2020.03.063 |
| URL / URN: | https://www.sciencedirect.com/science/article/pii/S221282712... |
| Abstract: | The rush of new providers of Industrial Internet of Things solutions and machine learning applications onto the market opens up new possibilities for data acquisition and analysis that go beyond the classical approach of model- and empirical-based process analysis. In this context, classic production tasks, e.g. quality assurance by random sampling, should be critically reviewed for their relevance. These non-value-adding activities can potentially be eliminated by disruptive digitalisation in order to increase labour productivity. This paper showcases the potential of a data-driven approach for quality determination in a drilling process using machine control data. |
| Uncontrolled Keywords: | Quality, Machine Learning, Drilling |
| Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > Management of Industrial Production 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > Machining Technology (2021 merged in TEC Fertigungstechnologie) |
| Date Deposited: | 08 Oct 2020 06:06 |
| Last Modified: | 08 Oct 2020 06:06 |
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