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Selection and Application of Machine Learning-Algorithms in Production Quality

: Krauß, Jonathan; Frye, Maik; Döhler Beck, Gustavo Teodoro; Schmitt, Robert H.

Fulltext ()

Beyerer, Jürgen (Ed.); Kühnert, Christian (Ed.); Niggemann, Oliver (Ed.):
Machine Learning for Cyber Physical Systems. Selected papers from the International Conference ML4CPS 2018 : Selected papers from the International Conference ML4CPS 2018, Karlsruhe, October 23rd and 24th, 2018
Berlin: Springer Vieweg, 2019 (Technologies for Intelligent Automation 9)
ISBN: 978-3-662-58484-2 (Print)
ISBN: 978-3-662-58485-9 (Online)
Conference on Machine Learning for Cyber-Physical-Systems and Industry 4.0 (ML4CPS) <4, 2018, Karlsruhe>
Conference Paper, Electronic Publication
Fraunhofer IPT ()

Due to the increase in digitalization Machine Learning (ML)-algorithms bare high potentials for process optimization in the production quality-domain. Nowadays, ML-algorithms are hardly implemented in the production environment. In this paper, we present a tangible use case in which ML-algorithms are applied for predicting the quality of products in a process chain and present the lessons learned we extracted from the application. In the de-scribed project, the process of choosing ML-algorithms was a bottleneck. There-fore we describe a promising approach how a decision making tool can help selecting ML-algorithms problem-specifically.