Cross sectional fiber morphology plays a key role for mechanical and optical paper properties. Current methods for the measurement of cross sectional morphology of pulp fibers e.g. fiber wall thickness, fiber wall area, fiber collapse have serious limitations. First, they are not able to measure a statistically meaningful number of fibers with reasonable effort. Second, most of these methods evaluate the apparent shape of the fiber cross section because they do not take into account, that images of fiber cross sections are skewed if the fiber's main axis is not perpendicular to the image plane. This error can only be detected and eliminated if cross sectional properties are measured from 3D datasets of pulp fibers.
Therefore, the main goal of the proposed project is to develop an efficient procedure that permits statistically meaningful and correct analysis of fiber cross sectional properties from 3D datasets. In order to obtain statistical significance a large number of fibers has to be analyzed with reasonable effort. Digitization will be obtained by a fully automated procedure (recently developed in another project) delivering high resolution 3D datasets. The main innovation of the proposed research is development and validation of an efficient and correct measurement method. The key issue here is development of novel image analytical algorithms, which provide fully automated detection of the fiber cross sections and tracking of these cross sections through a sequence of slice images. Current solutions require a considerable amount of user interaction, making them time consuming and costly. Full automation of this step will eliminate the bottleneck of the measurement. This will enable serious quantitative research regarding the effect of fiber cross sectional morphology on paper properties and the effect of pulping and stock preparation on fiber cross sectional morphology.
A consistent method will be developed that restores the true fiber cross sectional shape from the apparent shape.
A sampling strategy is to be worked out, which ensures that measurement results of fiber populations are statistically representative.
The obtained results will be verified with other scientific measurement concepts based on confocal laser scanning microscopy (CLSM) and scanning electron microscopy (SEM).
The new method will be used to research a previously hardly investigated subject:
Sequences of fiber images will be analyzed regarding the variation of the cross sectional properties like fiber collapse and wall dimensions. Inter-fiber variations have large impact on mechanical fiber properties like fiber flexibility, strength and conformability. Such analysis will thus provide novel data and identify a possibly important new aspect of fiber morphology.
The research will be carried out by two teams. The computer vision group will develop the image analysis part and the pulp and paper group will work out the fiber morphology part. The proposed project will continue the long lasting and fruitful cooperation between these two groups at Graz University of Technology.