CT-Data Analysis & Visualisation
Since its foundation in 2005 software development has been of great importance for of the research group computed tomography of the Upper Austria University of Applied Sciences - Wels Campus. The major aim in CT software development was and still is to develop non standard and highly innovative aglorithms for novel application areas of our project partners. In this context four applications were developed: SimCT is a fast GPU based simulator of a 3DCT device. Analyse_This! allows the evaluation of laser shots in a carbon matrix. nfinity facilitates data fusion of dual energy CT datasets as well as statistical analysis. Finally a comprehensive, plattform independent framework, integrating all former developments was designed to be the basis for all further implementations of the research group computed tomography: iAnalyse.
iAnalyse is a user friendly tool for scientific visualisation and 3D image processing. As graphical user interface the cross-plattform framework Qt is used, which facilitates an easy to use and attractive interface. In-house visualisation and image processing algorithms are supported by algorithms of the ITK and VTK toolkit, which make iAnalyse a powerful tool for both 3D visualisation and CT data analysis. iAnalyse is capable of loading various volume dataset formats as well as different surface model formats. It provides slice by slice navigation in its 2D views, common 3D navigation with arbitrary cutting planes in the 3D view, together with custom views for individual visualization. Information on the datasets, filters and ongoing tasks is provided in the tab control on the bottom of a dataset window. Besides its function as information central, the tab control is also used for transferfunction design in 3D visualizations. Beyond this basic framework, iAnalyse is easily extensible to add new functionality. It serves as central development platform of the research group computed tomography and therefore integrates all algorithms and methods developed within the group. With the expansion of the development team, iAnalyse is ready to leap to the next level. Take your chance to experience iAnalyse!
|Fiber-reinforced polymers play in many areas of application an interesting part. They are lightweight and thus unlock a huge potential for lightweight design. Through specific determination of the orientation of the fibers, characteristics such as strength are becoming important. Fiber-reinforced polymers are increasingly used for aircraft parts or in the automobile industry. Our Fiber extraction tool is able to extract the fibers and their properties from a volume dataset. Properties like length, diameter and orientation are calculated for every single fiber. And the fibers are visualized through straight lines and color coding (for the orientation).|
|Fiber-reinforced polymers belong to the group of high-performance composite materials and are characterized by their high mechanical strength at simultaneously low weight. Since the fibers in these materials strongly affect the mechanical properties such as stiffness, strength, ductility, etc., it is important to characterize them precisely in order to optimize the material systems.
FiberScout is designed to accurately analyze fiber-reinforced polymers with respect to their fiber properties (for example, distribution of fiber lengths and fiber orientation). The individual fibers can be selected and displayed by certain criteria. Fibers with similar characteristics can be grouped into classes and stored with additional statistical information in a list. Specific visualization methods simultaneously show the spatial position of all defined fiber classes in the CT volume data set.
|Mean objects (MObjects) are a novel way to explore a high number of individual objects in a dataset, e.g., pores, inclusions, particles, fibers, and cracks. After calculating the individual object properties volume, dimensions and shape factors, all objects are clustered into a MObject. With our software tool, the resulting MObject parameter space can be explored interactively. To do so, we introduce the visualization of mean object sets (MObject Sets) in a radial and a parallel arrangement. Each MObject may be split up into sub-classes by selecting a specific property, e.g., volume or shape factor, and the desired number of classes. Applying this interactive selection iteratively leads to the intended classifications and visualizations of defects in the dataset.|
|We present an advanced visualization method for the characterization of porosity in carbon fiber reinforced polymers (CFRP). Therefore we introduce Porosity Maps for a fast porosity evaluation of the specimen. Porosity maps show areas with high and low porosity. They are calculated for the three axis-aligned directions. In our novel interactive exploration pipeline the individual pores are filtered in two stages. After selecting a region of interest in the porosity maps, the pores are filtered with parallel coordinates according to their local properties, e.g. volume, dimension in x, y, z direction or shape factor.|
|A Simulation of a computed tomography scan before a real scan is a desirable feature for industrial use of the method. The main purpose of this research field is to determine optimal scan parameters for a specimen with predefined geometry and material. Optimal scan parameters allow to increase the contrast and the sharpness of features in acquired CT images and to reduce artifacts, which do not correspond to real features of the specimen. Furthermore, the knowledge of image acquisition and principles behind data generation is essential for the development and enhancement of methods or algorithms for CT.|