Quantitative X-ray tomography and deep learning analysis of damage topology in impact-loaded 3D-printed Bouligand composites

Abstract

It is well known that Bouligand structures derive their impact tolerance from low-pitch helicoidal fibre architectures that steer cracks and regulate damage growth. This study examines the low-velocity impact response of continuous aramid fibre-reinforced nylon composites manufactured by dual-extrusion fused filament fabrication (FFF) with pitch angles of 5°, 10°, 20°, and 45°. Instrumented impact testing is combined with post-mortem high-resolution X-ray computed tomography (XCT) to characterise how pitch angle influences impact response and damage topology. In particular, we use a deep neural network (DNN)-assisted segmentation framework to classify through-thickness cracking, delamination, and filament fracture. While previous studies have demonstrated the beneficial influence of low-pitch Bouligand architectures on impact resistance, the present work provides volumetric quantification of intralaminar cracking, delamination, and filament fracture within continuous-fibre FFF Bouligand structures, revealing how their interaction evolves with pitch angle. The results show that the lowest-pitch laminate exhibits the highest peak load and absorbed energy, while low-pitch architectures also promote more distributed damage and suppress filament fracture.