Prediction of viscosity, density and solids content in inks employed in printing industry production chain combining infrared and neural models

In this work, some characteristics of the black and whiteinks that are part of the graphic printing processes,calledrotogravure, were evaluated. In this process, the ink playsa decisive role in the quality of the material produced,therefore, its properties must be evaluated and guaranteedin order to obtain a product that works properly in theprinting process.Thus, an analytical method was devel-oped that combines infrared spectroscopy with ArtificialNeural Networks (ANN) to estimate the viscosity, densityand solids content of inks, having the advantage of provid-ing highly accurate results very quickly and with littlecomputational effort. The best models were those devel-oped for density, with average percentage errors of: 1% intraining and validation, and 2% in testof the black andwhite inks together; 1% in training and validation, and0.7% in test of theblack ink; 0.2% in training, 0.8% invalidation and 0.7% in test of white ink. The method de-veloped has the potential to be applied in printing indus-tries as an improvement for the production of high qualityrotogravure printed materia

Determination of color changes of inks on the uncoated paper with the offset printing during drying using artificial neural networks

This study attempts to determinate color changes based on time in inks applied on the surface of wood-free uncoated paper with offset printing during drying. This study consists of two main cases: (1) Experimental analysis: By preparing a test page according to the 12647-2 principle with an offset printing system, test prints were applied to 120 g/m(2) wood-free uncoated paper using an ECI 2002 CMYK test chart. Each press was measured being subject to process every 15 min in the first 2 h, then hour by hour between 2 and 12 h, then 4-4 h between 12 and 24 h, and then 6-6 h between 24 and 48 h. CIELAB and reflectance values between 380 and 720 nm of the target, 1,485 colors of the test chart were obtained. To see the drying and color changes of the ink on paper, changes were determined by printing on the paper and applying artificial neural network (ANN) to spectrophotometer data at the stated time intervals. (2) Empirical analysis: The use of the ANN has been proposed as numerical approach to get of empirical equations of color changes in inks applied on the surface of wood-free uncoated paper with offset printing during drying. Based on the outputs of the study, ANN model can be used to estimate the effects of digital proofing systems used in color management on print quality with high confidence with the use of the acquired equations without experimental study. In the study, as colors are defined in terms of wave length, in case, all wave lengths are taken into consideration, certain wave length changes have been taken into consideration