System for Assessing, Exploring and Monitoring Offset Print Quality

Variations in offset print quality relate to numerous parameters of printing press and paper. To maintain constant quality of products, press operators need to assess, explore and monitor print quality. This paper presents a novel system for assessing and predicting values of print quality attributes, where the adopted, random forests (RF)-based, modeling approach also allows quantifying the influence of different parameters. In contrast to other print quality assessment systems, this system utilizes common print marks known as double grey-bars. A novel virtual sensor for assessing the miss-registration degree of printing plates using images of double grey-bars is presented. The inferred influence of paper and printing press parameters on print quality shows correlation with known print quality conditions. Key–Words: Virtual sensor, Random Forests, Print quality assessment, Decision support system, Variable importance, Bar-code reader