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Git Repos exist both on [https://github.com/KIproBatt Github] and [https://gitlab.cc-asp.fraunhofer.de/KIproBatt FhG Gitlab]
 
Git Repos exist both on [https://github.com/KIproBatt Github] and [https://gitlab.cc-asp.fraunhofer.de/KIproBatt FhG Gitlab]
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About '''KIproBatt''' - ''Intelligent battery cell manufacturing with AI-supported process monitoring based on a generic system architecture''
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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.
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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.
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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.
     
Simon Stier
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