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

48 Citations (Scopus)
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
Pages (from-to)21-24
Number of pages4
JournalCIRP Annals
Volume69
Issue number1
DOIs
Publication statusPublished - 20 May 2020
Externally publishedYes

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