| Autor: |
Bischoff P; Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Maria-Reiche-Str. 2, 01109 Dresden, Germany.; Institute of Solid State Electronics, Technische Universität Dresden, 01069 Dresden, Germany.; Senodis Technologies GmbH, Manfred-Von-Ardenne-Ring 20 D, 01099 Dresden, Germany., Carreiro AV; Fraunhofer Portugal Center for Assistive Information and Communication Solutions-AICOS, 1649-003 Lisbon, Portugal., Schuster C; Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Maria-Reiche-Str. 2, 01109 Dresden, Germany., Härtling T; Fraunhofer Institute for Ceramic Technologies and Systems IKTS, Maria-Reiche-Str. 2, 01109 Dresden, Germany.; Institute of Solid State Electronics, Technische Universität Dresden, 01069 Dresden, Germany.; Senodis Technologies GmbH, Manfred-Von-Ardenne-Ring 20 D, 01099 Dresden, Germany.; Fraunhofer Portugal Center for Smart Agriculture and Water Management-AWAM, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal. |
| Abstrakt: |
Drop-on-demand printing using colloidal or pigmented inks is prone to the clogging of printing nozzles, which can lead to positional deviations and inconsistently printed patterns (e.g., data matrix codes, DMCs). However, if such deviations are detected early, they can be useful for determining the state of the print head and planning maintenance operations prior to reaching a printing state where the printed DMCs are unreadable. To realize this predictive maintenance approach, it is necessary to accurately quantify the positional deviation of individually printed dots from the actual target position. Here, we present a comparison of different methods based on affinity transformations and clustering algorithms for calculating the target position from the printed positions and, subsequently, the deviation of both for complete DMCs. Hence, our method focuses on the evaluation of the print quality, not on the decoding of DMCs. We compare our results to a state-of-the-art decoding algorithm, adopted to return the target grid positions, and find that we can determine the occurring deviations with significantly higher accuracy, especially when the printed DMCs are of low quality. The results enable the development of decision systems for predictive maintenance and subsequently the optimization of printing systems. |
