Understanding the relationship between slicing and measured fill density in material extrusion 3D printing towards precision porosity constructs for biomedical and pharmaceutical applications

Fill density is a critical parameter affecting the functional performance of 3D printed porous constructs in the biomedical and pharmaceutical domain. Numerous studies have reported the impact of fill density on the mechanical properties, diffusion characteristics and content release rates of constructs. However, due to the way in which slicing toolpath calculations are performed, there is substantial deviation between the measured and slicing fill density for relatively small sized constructs printed at low fill densities (high porosities). The purpose of the current study was to investigate this discrepancy using a combination of mathematical modeling and experimental validation. Methods: The open source slicer Slic3r was used to 3D print 20 mm × 20 mm × 5 mm constructs at three identified slicing fill density values, 9.58%, 20.36% and 32.33% (exact values entered into software), in triplicates. A mathematical model was proposed to accurately predict fill density, and the measured fill density was compared to both the predicted as well as the slicing fill density. The model was further validated at two additional slicing fill densities of 15% and 40%. The total material within the construct was analyzed from the perspective of material extruded within the beads as well as the bead to bead interconnects using the predictive model. Results: The slicing fill density deviated substantially from measured fill density at low fill densities with absolute errors larger than 26% in certain instances. The proposed model was able to predict fill density to within 5% of the measured fill density in all cases. The average absolute error between predicted vs. measured fill density was 3.5%, whereas that between slicing vs. measured fill density was 13%. The material extruded in the beads varied from 86.5% to 95.9%, whereas that extruded in the interconnects varied from 13.5% to 4.1%. Conclusions: The proposed model and approach was able to predict fill density to a reasonable degree of accuracy. Findings from the study could prove useful in applications where controlling construct fill density in relatively small sized constructs is important for achieving targeted levels of functional criteria such as mechanical strength, weight loss and content release rate.