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Image-Based predictive maintenance concept for inkjet printing of ceramic Inks

: Bischoff, Peter; Zeh, Christoph; Kroh, Christoph; Schuster, Christiane; Härtling, Thomas

Fulltext urn:nbn:de:0011-n-6427939 (242 KByte PDF)
MD5 Fingerprint: 6041955bb3269656e71b79650636d5ef
Created on: 9.11.2021

SMSI 2021, Sensor and Measurement Science International : Digital Conference, 3 - 6 May 2021
Wunstorf: AMA Service, 2021
ISBN: 978-3-9819376-4-0
Conference "Sensor and Measurement Science International" (SMSI) <2021, Online>
Conference Paper, Electronic Publication
Fraunhofer IKTS ()
predictive maintenance; machine learning; inkjet printing; ceramic pigment; image analysis

Ceramic inks can be used to mark metal sheets in hot forming for track-and-trace purposes. However, the ceramic pigments in the inks can lead to clogging of printer nozzles which results in loss of print quality. Here we report on a predictive maintenance concept including different machine- and deep-learning models as the basis of a print quality assurance strategy. Pixelwise image segmentation leads to detailed information about the printing results. The information is used to train a model, classifying the remaining useful lifetime until insufficient printing results.