Prediction of paperboard thickness and bending stiffness based on process data

Sammanfattning: Bending stiness is one of the most important mechanical properties in paperboard making,giving rigidity to panels and boxes. This property is currently only possible to measure bydestructive measure o the production line. The current quality control method is decient byassuming a non-realistic consistency of the paperboard properties along the machine direction.The objective of this thesis is to predict the thickness and bending stiness of the nal boardsfrom process data.Two modelling approaches are used: the rst model calculates the bending stiness from acalculated thickness, while the other one uses the measured baseboard thickness. Both modelsuse common inputs such as material properties and grammage measurement. The grammage istaken from the online baseboard measurement. The material properties come from laboratorymeasurements and assumptions. It is assumed that the density ratio between the outer andmiddle plies is constant for all product lines, at all times. The TSI of each ply is dened fromtensile testing experiments and nominal bending stiness. It is also assumed that the coatingdoes not contribute to bending stiness. The two models use equations based on laminatetheory assuming orthotropic layers and neglecting the interlaminar shear forces. The modelsuse data of two dierent natures: i.e. laboratory data and online data. Laboratory data is usedas a comparative to evaluate the models' performance of calculated values from online data.The results show various levels of prediction accuracy for dierent paperboard grades. Theaverage thickness predictions are all underestimations within a 5% error while the bendingstiness estimations vary much more from product to product; varying from 9% underestimationto 32% overestimation. The bending stiness prediction for CD is consistently higher thanfor MD for both models. Most product lines have better results with the calculated thickness,approach 1. The calculated thickness is always underestimated and bending stiness is overestimated,hence the better results with the rst approach.The most important conclusion from the models' results is the spread of laboratory measurements,when compared to the predicted values. The large variation most likely comes fromproduction, implying inconsistencies in the manufacturing process that are not accounted forby the models. These modelling approaches have failed to capture the production variationsbecause of the lack of input parameters.