Macroscopic mapping of microscale fibers in freeform injection molded fiber-reinforced composites using X-ray scattering tensor tomography

Fiber-reinforced composites deliver lightweight but strong structures that are crucial in applications ranging from aerospace to the automotive industry. The advent of freeform injection molding has made the manufacturing of complex fiber-reinforced composites with full design freedom possible. Prediction of the mechanical properties, dictated by the local microfiber orientation, is essential for the performance characterization of fiber-reinforced composites. However, with conventional microtomography, the required microscale spatial resolution and the macroscopic field of view for full-size fiber-reinforced composite pieces cannot be effectively decoupled. X-ray scattering tensor tomography enables non-destructive macroscopic mapping of the local microfiber orientation as well as their degree of alignment. Recent advancements in X-ray optics have significantly increased the acquisition speed, making the tensor tomography attractive for industrial applications. Nonetheless, integration of the tensor tomography within production lines requires a flexible and robust implementation. In this work, we demonstrate the potential of X-ray scattering tensor tomography for industrial applications by characterizing the microstructure of a centimeter-sized industrially relevant freeform injection molding fiber-reinforced composite sample. We also show that the tensor tomography is compatible with robotic arms, which can position and orient objects in three dimensions with high flexibility and therefore are ideal sample manipulators for the tensor tomography in industrial settings. The results obtained with the robotic arm are compared to those obtained with the state-of-the-art 2-axis sample manipulation scheme. The retrieved information is highly consistent and shows agreement also with structure tensor analyses of conventional microtomography data taken at selected regions of the sample for additional validation.