Canon Production printing develops high-end digital inkjet printer systems for the high-volume and graphic arts professional printing markets. Especially in the graphics arts market (large format, multi-pass) many different media are used: paper, PVC, canvas, textile, banner, etc.
For these multi-pass printers, the print carriage moves left-right and after each pass of the carriage, the medium makes a small step. The productivity (and quality) of the print depends on the step size. If the step size is larger or smaller than intended, this results in light or dark lines in the print and other print artifacts.
In order to reduce these print quality artifacts, printed (and scanned) markers are used to calibrate the medium and to measure and correct the media steps errors. However, correction has limitations, and it would be better to prevent the step error. This step accuracy has a large impact on the productivity and print quality of our printers.
The main goal of this PDEng project is to predict and prevent the media step size errors by using machine learning in combination with a large amount of data from a variety of data sources: printed markers, media handling sensors, temperature and humidity sensors, medium type, media settings, medium position, etc. The developed algorithm will have to control the paper steps, i.e., not just make predictions.
We foresee a combination of different machine learning techniques as a fitting approach for this challenge. The first part of the assignment will be to make an inventarisation of what techniques are feasible and what the pros and cons of the methods are. Examples of the techniques we are thinking about are deep learning, reinforcement learning and unsupervised data clustering.
A large amount of data is already available and more can easily be generated.
We are looking for a student that is able to quickly establish an overview the challenge, and from this is able to define an approach on how to significantly reduce the uncertainty in the media step size. In addition, the student is encouraged to make suggestions on optimizing existing infrastructure to get the optimal result. For instance, by adding new sensors or changing the media calibration approach.
Electronic Systems group at TU/e and Canon Production Printing
The Electronic Systems group consists of seven full professors, two associate professors, eight assistant professors, several postdocs, about 40 PDEng and PhD candidates and support staff. The ES group is world-renowned for its design automation and embedded systems research. It is our ambition to provide a scientific basis for design trajectories of electronic systems, ranging from digital circuits to cyber-physical systems. The trajectories are constructive and lead to high quality, cost-effective systems with predictable properties (functionality, timing, reliability, power dissipation, and cost).
Canon is global leader in consumer and professional imaging. One of Canon’s goals is to be the
#1 in printing. Our strategic imperative and incentive is to constantly look for opportunities to improve our organization, business, culture and brand and to proactively pursue our ambitions. Founded in 1877 in Venlo, the Netherlands, Canon Production Printing has a long history of technical innovation and development. A key asset is inkjet, a game-changing and widely applicable imaging technology. Our ambition is to build on our expertise in jetting for high-volume, high-speed printing and to position ourselves as a thought leader in jetting technology and applications. Jetting is key to our future, and we are energized by our exploration of its extensive possibilities.
We are looking for excellent candidates that add value to the ES group and match the following profile:
Do you recognize yourself in this profile and would you like to know more?
Please contact dr.ir. Sander Stuijk, s.stuijk[at]tue.nl, http://www.es.ele.tue.nl/~sander
For information about terms of employment, click here or contact Mrs. Linda van den Boomen, HR advisor l.j.c.v.d.boomen[at]tue.nl.
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