A slicer-agnostic tool for void characterization from slicer images and its correlation to build orientation and raster angle in FFF

Purpose To quantify the relationships between build orientation, raster angle and void characteristics on tensile performance of carbon fiber reinforced Nylon 6 (PA6-CF) Fused Filament Fabrication (FFF) tensile samples. This work aims to establish a foundation for predicting mechanical properties in a component at the process planning stage. Design/methodology/approach Tensile testing of PA6-CF samples printed on the Bambu Lab X1C system was performed following the ASTM D638 standard, with the elastic modulus calculations determined per the ASTM E111 standard. Toolpaths were generated using Bambu Studio, and the corresponding layer preview images were used as inputs for the developed Python-based void characterization and contact area algorithms. Findings This work successfully extracts key trends between build orientation, raster angle, void volume and contact area in FFF PA6-CF parts. PLA and PA6-CF results are also shown and indicate that the effect of filament type cannot be captured by simple scaling factors alone. Originality/value A comprehensive build strategy-mechanical performance dataset is produced for PA6-CF. This work presents a slicer-agnostic void characterization solution without parsing G-code or requiring imaging techniques of printed samples. Analysis of interlayer contact areas and orientation-dependent effective contact regions is possible with this approach. The framework is adaptable to future use of thermally aware bead size approximations and can provide valuable input for a machine learning model to predict tensile part performance. Clear nonlinear trends related to void volume and contact area are reported.