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Machine Learning for Enhanced Waste Quantity Reduction: Insights from the MONSOON Industry 4.0 Project

: Beecks, Christian; Devasya, Shreekantha; Schlutter, Ruben

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>
European Commission EC
H2020; 723650; MONSOON
Conference Paper, Electronic Publication
Fraunhofer FIT ()

The proliferation of cyber-physical systems and the advancement of Internet of Things technologies have led to an explosive digitization of the industrial sector. Driven by the high-tech strategy of the federal government in Germany, many manufacturers across all industry segments are accelerating the adoption of cyber-physical system and Internet of Things technologies to manage and ultimately improve their industrial production processes. In this work, we are focusing on the EU funded project MONSOON, which is a concrete example where production processes from different industrial sectors are to be optimized via data-driven methodology. We show how the particular problem of waste quantity reduction can be enhanced by means of machine learning. The results presented in this paper are useful for researchers and practitioners in the field of machine learning for cyber-physical systems in data-intensive Industry 4.0 domains.