Machine learning approach for systematic analysis of energy efficiency potentials in manufacturing processes: A case of battery production

Sebastian Thiede, Artem Turetskyy, Thomas Loellhoeffel, Arno Kwade, Sami Kara, Christoph Herrmann

Research output: Contribution to journalArticleAcademicpeer-review

1 Downloads (Pure)

Abstract

Energy efficiency in manufacturing plays a crucial role in decreasing manufacturing costs and reducing environmental footprint. This is particularly important for producing battery cells with novel processes due to their cost-sensitivity and high potential impact on the environment. Therefore, design and operation of these processes are critical and require a high level of process and machine specific understanding. A methodology based on machine learning is presented, which has the capability of identifying improvement potentials using machine and process specific influencing factors. A battery production case is used to demonstrate the accuracy, transferability and validity of the methodology.
Original languageEnglish
JournalCIRP Annals
DOIs
Publication statusE-pub ahead of print/First online - 20 May 2020
Externally publishedYes

Fingerprint Dive into the research topics of 'Machine learning approach for systematic analysis of energy efficiency potentials in manufacturing processes: A case of battery production'. Together they form a unique fingerprint.

  • Cite this