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Data-Based Process Analysis in Machining Production: Case Study for Quality Determination in a Drilling Process

Ziegenbein, Amina and Fertig, Alexander and Metternich, Joachim and Weigold, Matthias (2020):
Data-Based Process Analysis in Machining Production: Case Study for Quality Determination in a Drilling Process.
In: Procedia CIRP, 93, pp. 1472-1477. Elsevier B.V., ISSN 22128271,
DOI: 10.1016/j.procir.2020.03.063,
[Article]

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 and Fertig, Alexander and Metternich, Joachim and Weigold, Matthias
Title: Data-Based Process Analysis in Machining Production: Case Study for Quality Determination in a Drilling Process
Language: English
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.

Journal or Publication Title: Procedia CIRP
Journal volume: 93
Publisher: Elsevier B.V.
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
Date Deposited: 08 Oct 2020 06:06
DOI: 10.1016/j.procir.2020.03.063
Official URL: https://www.sciencedirect.com/science/article/pii/S221282712...
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