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{{DISPLAYTITLE:KIproBatt Project Wiki}}{{AccessControl/Public}}

Project Wiki and Semantic Dataspace based on [https://github.com/OpenSemanticLab OpenSemanticLab] for the [[Fraunhofer ISC/Processes/KIproBatt v1/Process|KIproBatt Cell Manufacturing (public)]] and [[KIproBatt|Project Management (private)]].

To access the [[JupyterLab]] Workflow Environment, visit [https://kiprobatt.de/jupyter kiprobatt.de/jupyter (private)]

Git Repos exist both on [https://github.com/KIproBatt Github] (archived dataset) and [https://gitlab.cc-asp.fraunhofer.de/KIproBatt FhG Gitlab] (data & AI tools). <br>

For citing the platform, please use https://doi.org/10.1002/ente.202301305.

If you reuse our published data, consider citing our data repository: https://doi.org/10.5281/zenodo.8418323  .

<br />

==What can I do here?==
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      |title=Glossary 
      |description=Take a look at our interactive glossary - especially the [[Battery value chain/Process/Cell manufacturing|Cell manufacturing]]
      |link=[[Battery value chain|Open Glossary]]
    }}
    {{OslTemplate:Helper/UI/Tiles/Tile
      |image=🔁
      |icon2=🔋
      |title=Process Spec
      |description=Explore our Semantic Process Definition - from the [[Fraunhofer ISC/Processes/KIproBatt v1/Process|top]] down to [[Fraunhofer ISC/Processes/KIproBatt v1/Parameter/Drying/Temperature|single parameters]]
      |link=[[Fraunhofer ISC/Processes/KIproBatt v1/Process#Graph|Show Spec]] 
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      |title=Correlations
      |description=Follow the correlations we have classified as [[Parameter_correlations/High_priority|important]]
      |link=[[Parameter_correlations/High_priority|View Map]]
      |tooltip=
      |link_label=View Map
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    {{OslTemplate:Helper/UI/Tiles/Tile
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      |title=Production Knowledge Graph
      |description=View the Knowledge Graph of a single produced cell
      |link_label=Show Graph
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      |description=View the Semantic Metadata Structure of an AI based data analysis
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<!--       |icon1= -->
      |image=📊<!-- ❌ 📈📉-->
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      |title=KPIs
      |description=Inspect our [[KPI|Key Performance Indicators (KPIs)]]
<!--       |url=/wiki.. -->
      |link=[[KPI|Show KPIs]]
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      |link_label=Show KPIs
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<!-- *Take a look at our interactive [[Battery value chain]] glossary - especially the [[Battery value chain/Process/Cell manufacturing|Cell manufacturing]] from [[Battery value chain/Process/Cell manufacturing/Separation|Separation]] to [[Battery value chain/Process/Cell manufacturing/EOL Testing|EOL_Testing]]
*Explore our Semantic Process Definition - from the [[Fraunhofer ISC/Processes/KIproBatt v1/Process|top]] down to [[Fraunhofer ISC/Processes/KIproBatt v1/Parameter/Drying/Temperature|single parameters]]
*Follow the correlations we have classified as [[Parameter_correlations/High_priority]]
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AI image segmentation] and 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electrochemical feature extraction]
*Inspect our [[KPI|Key Performance Indicators (KPIs)]]
*View related ontologies at [https://onto-wiki.eu onto-wiki.eu]
*... even more in the future -->

<br />
==About '''KIproBatt''' - ''Intelligent battery cell manufacturing with AI-supported process monitoring based on a generic system architecture''==
<br />

====Funding authority:====
BMBF InZePro Cluster
<br />

====Project duration:====
10/1/2020 – 3/31/2024

====Partners:====

*Fraunhofer Institute for Silicate Research ISC
*Fraunhofer Institute for Casting, Composite and Processing Technology IGCV
*University of Bayreuth
*Aschaffenburg University of Applied Sciences (UAS)

====Contact:====

*Simon Stier: simon.stier@isc.fraunhofer.de

====Abstract:====
Due to its complexity and vast economic as well as ecological impact,  the Li-ion battery cell production process is subject to ongoing digitization  and optimization in order to increase cell performance while reducing  resource consumption and production costs.

In this context, artificial intelligence (AI) holds immense potential  in leveraging manufacturing data to improve the cell production process.  Hence, we aim to enhance cell production with AI-based end-to-end process  monitoring, which covers all steps of the process chain. For this purpose, we  develop a generic, software-implemented system architecture as reusable  structure that allows us to connect the process data acquisition with a  carefully constructed ontology-based semantic data space. Based on this  system architecture, we attach machine learning approaches from two  perspectives: In the first perspective, we apply both data and physics driven  models to specific process parameters to detect and evaluate correlations and  process anomalies. In the second perspective, we develop an overarching  end-to-end process monitoring. For this application, we integrate the  previously developed models into a dashboarding system in order to assess  their relevance for cell performance and monitor the cells' state in  production constantly and as real-time as possible.

The combination of these two perspectives allows us to detect defects early in the production process, rapidly increase the quality performance and derive flexible adjustments to the process parameters in case of malfunction or defects. Thus, we expect to reduce the total cost of cell production as well as improve its carbon footprint by reductions in resource and energy consumption. Finally, the generic, semantically structured design enables an easy transfer to other research and industrial processes.

==About the '''KIproBatt Dataspace'''==


Technically the KIproBatt plattform consists of the following OpenSource components

*MediaWiki: Most important Wiki framework, basis of Wikipedia
*Semantic MediaWiki: Semantic extension for MediaWiki
*JupyterLab: Cloud-based scripting environment, e. g. for Python

MediaWiki provides the basic document-based data structure. Furthermore, extensive interfaces for human (editors) and machine (API) are provided. Templates and forms ensure uniformity and efficiency. The extension Semantic MediaWiki provides the Wiki with numerous possibilities for linking and annotating data. In addition, semantic queries can be sent directly to the Wiki and results can be visualized in a variety of ways. Via JupyterLab users can access data stored in the Semantic MediaWiki, process it and push the results back to the Wiki.


This project is funded by the German Federal Ministry of Education and Research (BMBF), grant no. 03XP0309A-C.
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https://kiprobatt.de/w/pub/BMBF_gefoerdert_2017_en.jpg
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