IS-lab projects

Uncertainty about the main reasons causing too high quality variations of colour prints; lack of comprehensive knowledge of interaction between paper, printing press, and ink; lack of robust tools for print quality predictions; lack of tools capable of explaining which technological process parameters are failing when the overall print quality is insufficient are the main industrial problems addressed in the project.
 
Identification of the measurable print quality attributes and the main process parameters affecting the attributes, methods for mapping values of process parameters into the space of the print quality attributes and the overall print quality score, an integrated decision support system capable of taking queries from and providing recommendations to a papermaker, printer, and ink supplier are the scientific problems addressed.
 
Multivariate statistical analysis, artificial neural networks based image and signal analysis, optical sensors for high resolution online measurements, multidimensional scaling, multi-criteria decision making, fuzzy sets, learning systems are the main scientific approaches to problems addressed.
 
The expected results and benefits of the project are: deep knowledge of paper, printing press, and ink interaction allowing to explore economically plausible means of adjusting technological parameters for optimizing the papermaking and printing processes; a comprehensive database containing parameters obtained from online as well as off-line measurements characterizing paper, printing press, printing process, and ink; data-mining tools and means for automatic database update; an integrated multipurpose decision support system; a tool for predicting the print quality score from a set of process parameters; insight into which technological parameters are failing when the overall print quality is insufficient; a unique test facility; well established collaboration between university researchers and the industries involved in the printed media production process.