A demonstrator for deep learning-based visual inspection was developed to showcase the implementation of an end-to-end data pipeline for an exemplary use case for defect detection in battery cell manufacturing.
| Topic Fields | |
| Published | 2024 |
| Involved Institutes | |
| Project Type | ICNAP Research/Transfer Project |
| Responsibles |
Automating visual inspection tasks holds great potential in production environments. In this project, a demonstrator was developed that covers the whole end-to-end data pipeline. It includes image acquisition via a camera, the data connection via the Fraunhofer Edge Cloud, the analysis using a deep learning model and the visualization in a frontend for the operator. An exemplary use case was implemented, which aims to detect defects in the laser welding process of battery cells. In the future, more use cases can be integrated into the demonstrator due to its modular hardware and software design.
Contact us to get in touch! With a membership, you’ll gain full access to all project information and updates.
© Fraunhofer 2025
