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2020
Conference Paper
Titel

Automated continuous learn and improvement process of energy efficiency in manufacturing

Abstract
Optimizing the energy efficiency of machine tools automatically is promising. There are several metrics to be considered when it comes to automated optimization approaches in serial production which are especially quality, technical availability, and cycle time. These are not supposed to be impaired whereas they are indicated as a central obstacle. The measurements and the machine data show the actions happening in the machine which also leads to the data-driven traceability of machine states. This article presents a method to formulate the necessary expert knowledge to optimize the energy efficiency of a machine tool and is basically done by a decision tree which leads to a set of rules which will be explained in this article. This set of rules coordinate an optimization algorithm, which technically manipulates selected variables under the given rules. The development and is a result of a research which was done at the serial production of camshafts at the MB plant in Berlin.
Author(s)
Can, A.
Fisch, J.
Stephan, P.
Thiele, G.
Krüger, J.
Hauptwerk
46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020. Proceedings
Konferenz
IEEE Industrial Electronics Society (IECON Annual Conference) 2020
Thumbnail Image
DOI
10.1109/IECON43393.2020.9255088
Language
English
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Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK
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