Robust design optimization of Critical Quality Indicators (CQIs) of medical-graded polycaprolactone (PCL) in bioplotting

Polycaprolactone (PCL), either in its pure grade or as a polymeric matrix for bio-composites, plays a key role in the biomedical and bioengineering industries. It is also considered a multifunctional and versatile polymer for bioprinting and bioplotting purposes, especially in tissue engineering. Herein, an undiscovered yet valuable aspect of PCL extrusion-based bioprinting, such as the predictability of Critical Quality Indicators (CQIs), is investigated in depth. With the aid of the robust L25 orthogonal matrix design, the six most generic and device-independent control factors proved their impact on quality metrics such as global porosity, dimensional conformity, and surface roughness, determined with the aid of highly evolved Nondestructive Testing (NDT) and algorithms. To this end, 25 experimental runs were set, and 125 specimens were fabricated using an industrial-scale bio-plotter and medical-graded polycaprolactone. Various infill densities (ID), layer thicknesses (LT), raster deposition angles (RDA), printing speeds (PS), nozzle temperatures (NT), and bed temperatures (BT) were applied. CQIs were determined using optical profilometry and microscopy, and micro-computed tomography. Quadratic predictive equations were compiled and verified using two additional, well-chosen experimental runs. These generally applicable predictive models carry a massive amount of research and industrial merit, as they ensure visibility in bioprinting with PCL.