Development of a Semantic Database Model to Facilitate Data Analytics in Battery Cell Manufacturing

Ozan Yesilyurt, David Brandt, Julian Grimm, Kamal Husseini, Aleksandra Naumann, Aleksandra Naumann, Julia Meiners, Julia Meiners, David Becker-Koch

2022

Abstract

The global demand for batteries is increasing worldwide. To cover this high battery demand, optimizing manufacturing productivity and improving the quality of battery cells are necessary. Digitalization promises to offer great potential to address these challenges. Through data collection along the manufacturing processes, hidden correlations can be identified. However, data is highly diverse in battery cell manufacturing, complicating data analysis. A semantic data storage can increase the understanding of the relationships between the datasets, facilitating the identification of the causes of defects in manufacturing processes. To structure heterogeneous data in a semantically understandable and analyzable form, this paper presents the development of a semantic database model. The realization of this model enables structuring various datasets for simplified access and usage for increasing productivity and battery cell quality in battery cell manufacturing.

Paper Citation

in Harvard Style

Yesilyurt O., Brandt D., Grimm J., Husseini K., Naumann A., Meiners J. and Becker-Koch D. (2022). Development of a Semantic Database Model to Facilitate Data Analytics in Battery Cell Manufacturing. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-583-8, pages 13-20. DOI: 10.5220/0011139500003269

in Bibtex Style

@conference{data22,
author={Ozan Yesilyurt and David Brandt and Julian Grimm and Kamal Husseini and Aleksandra Naumann and Julia Meiners and David Becker-Koch},
title={Development of a Semantic Database Model to Facilitate Data Analytics in Battery Cell Manufacturing},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2022},
pages={13-20},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011139500003269},
isbn={978-989-758-583-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Development of a Semantic Database Model to Facilitate Data Analytics in Battery Cell Manufacturing
SN - 978-989-758-583-8
AU - Yesilyurt O.
AU - Brandt D.
AU - Grimm J.
AU - Husseini K.
AU - Naumann A.
AU - Meiners J.
AU - Becker-Koch D.
PY - 2022
SP - 13
EP - 20
DO - 10.5220/0011139500003269