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10/16/2024 13:06

Deep learning for real-time quality control: Aachen-based Fraunhofer Institutes improve defect detection in weld seams

Susanne Krause Externe und interne Kommunikation
Fraunhofer-Institut für Produktionstechnologie IPT

    The International Center for Networked, Adaptive Production (ICNAP) of the three Aachen-based Fraunhofer Institutes will be presenting an AI-supported analysis system for visual quality control at the SPS trade fair in Nuremberg from November 12 to 14, 2024. In hall 6, booth 6-357, ICNAP will be demonstrating how deep learning can be used to check weld seams on battery cell modules for defects in real time.

    The use case that the researchers will be demonstrating at the SPS trade fair in Nuremberg shows the automated detection of defects in weld seams on battery cell modules. To do this, the battery cell module is manually placed under a camera and a photo is taken, which is then analyzed by a pre-trained deep learning model. During ongoing production, the artificial intelligence independently detects defects that would otherwise have to be identified by trained specialists. The new system, on the other hand, immediately displays the defect detection in color in real time – red for a defective product and green for a flawless product. This allows defects to be detected during the production process and rejects to be avoided at an early stage.

    The system presented here specifically inspects battery cell modules, but it can also be applied to numerous other products that require a visual surface inspection. The process increases production efficiency through consistent and objective quality assurance and relieves skilled workers of this routine task. By automating the inspection process, companies can also counter future staff shortages.

    The system's error rate continues to fall over time thanks to a growing volume of training data. Data analysis can take place either directly on site on the shop floor or using external cloud systems. During the SPS, the researchers use their new "FCTRY CLD", a local real-time capable cloud from the Fraunhofer IPT. This enables the operation of time-critical applications for production, such as the control of machines or the processing of sensor data. The advantage: the data is processed securely in the production environment and does not leave the company, as is the case with public cloud providers.

    ICNAP: A strong network for the digital future

    The International Center for Networked Adaptive Production, ICNAP, is a collaboration between the three Aachen-based Fraunhofer Institutes for Production Technology IPT, for Laser Technology ILT and for Molecular Biology and Applied Ecology IME and 24 renowned companies worldwide. ICNAP was founded as an open research community that develops and tests new approaches to digitalization in production as an industrial test environment.

    Visitors are cordially invited to visit the ICNAP booth at SPS 2024 in hall 6, booth 6-357 to discover the potential of deep learning and the advantages of the new "FCTRY CLD" for their production in person.


    Contact for scientific information:

    Dr.-Ing. Mario Pothen
    Contact person for Digitalization & Networking

    Fraunhofer Institute for Production Technology IPT
    Steinbachstrasse 17
    Phone +49 241 8904-144
    mario.pothen@ipt.fraunhofer.de
    www.ipt.fraunhofer.de/en.html


    More information:

    https://www.ipt.fraunhofer.de/en/Press/Pressreleases/241016-deep-learning-for-re...


    Images

    Automatic defect detection on the weld seams of battery cell modules.
    Automatic defect detection on the weld seams of battery cell modules.

    © Fraunhofer IPT

    The system developed by ICNAP uses deep learning to detect surface defects in real-time.
    The system developed by ICNAP uses deep learning to detect surface defects in real-time.

    © Fraunhofer IPT


    Criteria of this press release:
    Journalists
    Economics / business administration, Electrical engineering, Information technology, Materials sciences, Mechanical engineering
    transregional, national
    Transfer of Science or Research
    English


     

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