Process parameter optimization for reproducible fabrication of layer porosity quality of 3D-printed tissue scaffold

Bioprinting, or bio-additive manufacturing, is a critical emerging field for transforming tissue engineering regenerative medicine to produce biological constructs and scaffolds in a layerwise fashion. Geometric accuracy and spatial distribution of scaffold porosity are critical factors associated with the quality of bio-printed tissue scaffolds. Determining optimal process parameters for tissue scaffold microextrusion 3D printing by traditional trial-and-error approaches is costly, labor-intensive, and time-consuming. In addition, effective in-process sensing techniques are needed to observe internal multilayer scaffold structures, such as porosity. Therefore, an in-process sensing platform based on integrated light scanning and microscopy was proposed to acquire in-process layer information during the fabrication of polymeric and hydrogel scaffolds. This work implements a customized sensing platform consisting of a 3D scanner and digital microscope for in-process quality monitoring of tissue scaffold biofabrication that provides in situ characterization of each printed layer’s quality conditions (e.g., porosity). The proposed sensor-based in-process quality monitoring system can accurately capture layerwise porosity quality. Design of experiments (DoE) experimental analysis yielded a set of optimal process parameters that significantly improved the geometric accuracy and compressive modulus of thermoplastic- and hydrogel-based tissue scaffolds. The developed sensing system coupled with the DoE approach enables effective process parameter optimization to fabricate porous 3D-printed tissue scaffolds. It can significantly improve the quality and reproducibility of research associated with porous 3D-printed products, such as tissue scaffolds and membranes.