Deploying Virtual Quality Gates in a Pilot-Scale Lithium-Ion Battery Assembly Line
Source abstract
Abstract
Pilot production is a critical transitional phase in the process of new product development or manufacturing, aiming at ensuring that products are thoroughly validated and optimized before entering full-scale production. During this stage, a key challenge is how to leverage limited resources to build data infrastructure and conduct data analysis to establish and verify quality control. This paper presents the implementation of a cyber–physical system (CPS) for a lithium battery pilot assembly line. A machine learning-based predictive model was employed to establish quality control mechanisms. Process knowledge-guided data analysis was utilized to build a quality prediction model based on the collected battery data. The model-centric concept of ‘virtual quality’ enables early quality judgment during production, which allows for flexible quality control and the determination of optimal process parameters, thereby reducing production costs and minimizing energy consumption during manufacturing.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Product Development and Customization · Flexible and Reconfigurable Manufacturing Systems · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Engineering and Computer Science, Aschaffenburg University of Applied Sciences, Würzburger Str. 45, 63743 Aschaffenburg, Germany
Fraunhofer Institute for Silicate Research ISC, Neunerpl. 2, 97082 Würzburg, Germany
Funding
German Federal Ministry of Education and Research (BMBF)
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