Deep learning-assisted real-time defect detection and process control for electrode manufacturing of lithium-ion battery cells
Publication date
2025-05-27
Document type
Konferenzbeitrag
Author
Kampker, Achim
Heimes, Heiner Hans
Born, Henrik
Schmied, Jessica
Li, Rui Yan
Özcan, Mert
Organisational unit
Chair of Production Engineering of E-Mobility Components (PEM), RWTH Aachen University
Publisher
Universitätsbibliothek der HSU/UniBw H
Book title
Machine learning for cyber physical systems : proceedings of the conference ML4CPS 2025
First page
22
Last page
33
Part of the university bibliography
Nein
Language
English
Keyword
Deep learning
Machine vision
Battery cell production
Digitalization
Abstract
Detecting and preventing defects on electrode surfaces during the manufacturing of lithium-ion battery cells remains a crucial challenge to avoid further cascading effects in subsequent stages of the manufacturing chain. Variations in surface quality or individual contamination can adversely affect battery performance and lifespan, potentially posing safety risks. This paper presents a deep-learning assisted system for detection and classification of coating defects in battery electrodes and subsequent process optimization strategies. Following improvement of product quality and a reduction of the reject rate in the coating and drying process of electrodes, the research contributes to the enhancement of overall efficiency in lithium-ion battery cell manufacturing. To validate the practical application of the system, a case study is conducted in the coating and drying processes of the battery cell pilot line production CELLFAB of the RWTH Aachen University. The results indicate great potential for enhancement of real-time defect detection and further optimization of process parameters.
Version
Published version
Access right on openHSU
Open access