Data-driven cyber-physical system for quality gates in lithium-ion battery cell manufacturing

Artem Turetskyy; Jacob Wessel; Christoph Herrmann; Sebastian Thiede

doi:10.1016/j.procir.2020.03.077

Data-driven cyber-physical system for quality gates in lithium-ion battery cell manufacturing

Artem Turetskyy^*
, Jacob Wessel
, Christoph Herrmann
, Sebastian Thiede

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › Academic › peer-review

36 Citations (Scopus)

63 Downloads (Pure)

Abstract

In the production chain of Lithium-ion battery (LIB) cells, various processes influence intermediate product features, which then influence the LIB performance. It is important to know these influences in order to improve product quality and to control the production. This paper presents a concept for a data-driven cyber-physical system based on quality gates in LIB cell manufacturing. The concept utilizes data-driven modelling in order to predict the performance of future LIB cells by using quality gates of intermediate product features. This prediction of product performance enables a better assessment of intermediate product quality and helps deriving recommendations for latter processes.

Original language	English
Pages (from-to)	168-173
Number of pages	6
Journal	Procedia CIRP
Volume	93
DOIs	https://doi.org/10.1016/j.procir.2020.03.077
Publication status	Published - 22 Sept 2020
Externally published	Yes
Event	53rd CIRP Conference on Manufacturing Systems, CIRP CMS 2020 - Virtual Conference, Chicago, United States Duration: 1 Jul 2020 → 3 Jul 2020 Conference number: 53

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure

Keywords

Cyber-physical systems
Data mining
Lithium-ion battery cells
Manufacturing
Quality gates

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10.1016/j.procir.2020.03.077Licence: CC BY-NC-ND

1-s2.0-S2212827120307009-mainFinal published version, 1.4 MBLicence: CC BY-NC-ND

Cite this

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title = "Data-driven cyber-physical system for quality gates in lithium-ion battery cell manufacturing",

abstract = "In the production chain of Lithium-ion battery (LIB) cells, various processes influence intermediate product features, which then influence the LIB performance. It is important to know these influences in order to improve product quality and to control the production. This paper presents a concept for a data-driven cyber-physical system based on quality gates in LIB cell manufacturing. The concept utilizes data-driven modelling in order to predict the performance of future LIB cells by using quality gates of intermediate product features. This prediction of product performance enables a better assessment of intermediate product quality and helps deriving recommendations for latter processes.",

keywords = "Cyber-physical systems, Data mining, Lithium-ion battery cells, Manufacturing, Quality gates",

author = "Artem Turetskyy and Jacob Wessel and Christoph Herrmann and Sebastian Thiede",

year = "2020",

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day = "22",

doi = "10.1016/j.procir.2020.03.077",

language = "English",

volume = "93",

pages = "168--173",

journal = "Procedia CIRP",

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note = "53rd CIRP Conference on Manufacturing Systems, CIRP CMS 2020, CIRP CMS 2020 ; Conference date: 01-07-2020 Through 03-07-2020",

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AU - Turetskyy, Artem

AU - Wessel, Jacob

AU - Herrmann, Christoph

AU - Thiede, Sebastian

N1 - Conference code: 53

PY - 2020/9/22

Y1 - 2020/9/22

N2 - In the production chain of Lithium-ion battery (LIB) cells, various processes influence intermediate product features, which then influence the LIB performance. It is important to know these influences in order to improve product quality and to control the production. This paper presents a concept for a data-driven cyber-physical system based on quality gates in LIB cell manufacturing. The concept utilizes data-driven modelling in order to predict the performance of future LIB cells by using quality gates of intermediate product features. This prediction of product performance enables a better assessment of intermediate product quality and helps deriving recommendations for latter processes.

AB - In the production chain of Lithium-ion battery (LIB) cells, various processes influence intermediate product features, which then influence the LIB performance. It is important to know these influences in order to improve product quality and to control the production. This paper presents a concept for a data-driven cyber-physical system based on quality gates in LIB cell manufacturing. The concept utilizes data-driven modelling in order to predict the performance of future LIB cells by using quality gates of intermediate product features. This prediction of product performance enables a better assessment of intermediate product quality and helps deriving recommendations for latter processes.

KW - Cyber-physical systems

KW - Data mining

KW - Lithium-ion battery cells

KW - Manufacturing

KW - Quality gates

UR - https://www.scopus.com/pages/publications/85092433365

U2 - 10.1016/j.procir.2020.03.077

DO - 10.1016/j.procir.2020.03.077

M3 - Conference article

AN - SCOPUS:85092433365

SN - 2212-8271

VL - 93

SP - 168

EP - 173

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 53rd CIRP Conference on Manufacturing Systems, CIRP CMS 2020

Y2 - 1 July 2020 through 3 July 2020

ER -

Data-driven cyber-physical system for quality gates in lithium-ion battery cell manufacturing

Abstract

UN SDGs

Keywords

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