Data fusion of surface data sets of X-ray computed tomography measurements using locally determined surface quality values

Abstract

X-ray computed tomography as a measurement system faces some difficulties concerning the quality of the acquired measurements due to energy-dependent interaction of polychromatic radiation with the examined object at hand. There are many different techniques to reduce the negative influences of these artefact phenomena, which is also the aim of this newly introduced method. The key idea is to create several measurements of the same object, which only differ in their orientation inside the ray path of the measurement system. These measurements are then processed to selectively correct faulty surface regions. To calculate the needed geometrical transformations between the different measurements with the goal of a congruent alignment in one coordinate system, an extension of the iterative closest point (ICP) algorithm is used. To quantitatively classify any surface point regarding its quality value to determine the individual need of correction for each point, the local quality value (LQV) method is used, which has been developed at the Institute of Manufacturing Metrology. Different data fusion algorithms are presented whose performances are tested and verified using nominal–actual comparisons.