DMCGridFinder 0.0.1

Approximate DMC target positions from sets of coordinates

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

by Peter Bischoff, André V. Carreiro, Christiane Schuster, Thomas Härtling

This Paper has been submitted for publication in the "Journal of Imaging" on May 30th 2023.

Abstract

Drop-on-Demand printing using colloidal or pigmented inks is prone to 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 plan 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 to calculate the printed position, the target position, and the deviation of both for complete DMCs. Hence, our method focuses on the evaluation of the print quality, not on decoding of DMCs. We compare our results to state-of-the-art recognition and decoding algorithms 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.

Installation

You'll need a working Python environment to run the code. To create a Python environment, which does not interfere with your systems installation of Python, we recommend using Anaconda.

To create a new environment and install the package, from the root of this repository run:

conda create gridfinder python=3.9
pip install . --upgrade

Recreating the Results

To recreate the results run python runtime.py and python simulation.py. This will create the results described in section 3.1 and 3.2.
To create the results from section 3.3 additional data and source code is needed, which cannot be published. Please contact the authors for details.

Usage

Example:

from gridfinder.unguided import UGAT
targets = UGAT.approximate_grid(blob_positions) 

License

All source code is made available under a MIT license. See LICENSE.md for the full license text.