Quantitative X-ray tomography of advanced polymer composites

Advanced composite materials (ACMs) typically contain two or more constituents, such as matrix, fibers, inclusions and pores, with different physical and chemical characteristics. When combined, they produce a material with unique properties in terms of weight, strength, stiffness, or corrosion resistance. To inspect and study their 3D internal structure in a non-destructive way, the ACMs are imaged using X- ray computed tomography, in which a 3D dataset is reconstructed from the X-ray radiographs. The 3D dataset is subsequently further processed and analyzed in multiple sequential steps. This conventional workflow, however, suffers from inaccurate modeling and error propagation, which severely limits the accuracy with which ACM parameters of interest can be estimated. In this project, we will develop a paradigm shifting approach in which the quantification of ACM parameters is substantially improved. This will be realized by a novel workflow 1) accounting for possible deformation of the ACM during scanning and thereby reducing image reconstruction artefacts; 2) accurately modelling all constituents of the ACM (matrix, pores, inclusions and fibers); 3) directly estimating the ACM model parameters from the X-ray radiographs and thereby preventing error propagation by providing a feedback mechanism; 4) analyzing the workflows input parameter space with respect to sensitivity and stability of output parameters / characteristics of interest. Such a framework is up to now unprecedented. If successful, our framework will to provide substantially more accurate characterizations of internal structures of the ACMs in comparison to conventional workflows.