Quantifying the Displacement of Data Matrix Code Modules: A Comparative Study of Different Approximation Approaches for Predictive Maintenance of Drop-on-Demand Printing Systems

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...

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Vydané v:Journal of imaging Ročník 9; číslo 7; s. 125
Hlavní autori: Bischoff, Peter, Carreiro, André V., Schuster, Christiane, Härtling, Thomas
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 21.06.2023
MDPI
Predmet:
Algorithms
Approximation theory
Clustering
code recognition
Codes
Comparative studies
data matrix
Data matrix coding
Decoding
Deviation
drop deviation
Electronics in printing
Inks
Maintenance and repair
Matrix codes
Methods
Optimization
pattern recognition
Predictive maintenance
Printers (data processing)
Printing
Printing machinery and supplies
quality assessment
Quality control
Quality standards
Germany
code recognition
drop deviation
data matrix
predictive maintenance
quality assessment
pattern recognition
ISSN:2313-433X, 2313-433X
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Popis
Shrnutí: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.
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SourceType-Scholarly Journals-1
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ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging9070125