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Defect Detection and Process Control in Biological Additive Manufacturing
The long-term goal of this work is to facilitate flaw-free extrusion-based (bio)printing (EBB) of tissue engineering scaffolds. Towards this end goal, this dissertation aims to understand the fundamental process phenomena behind defect formation and to predict flaw occurrence and quality during or prior to the printing process. Biological additive manufacturing (Bio-AM) is the coupling the conventional additive manufacturing technology, biomaterials, and tissue engineering practices to yield biocompatible scaffold structures. Bio-AM holds the capacity to revolutionize healthcare. Two central innovations with Bio-AM are: 1) patient-specific organs with patient-derived cells and 2) drug and disease research in three dimensions. Notably, implants with patient-derived cells would not elicit an immune response, negating the need for life-long anti-rejection medication. While Bio-AM encompasses many modalities, this dissertation solely focuses on the most widely adopted form, EBB. Despite significant advancements in EBB systems in terms of their resolution, speed, compatible (bio)inks, and the level of achievable structural complexity, the process is still prone to defect formation. Defects such as strand heterogeneity, strand fusion, and improper pore geometry pose a significant risk to the functionality of printed parts. Further, process optimization is insufficient to negate defect occurrence. As such, there is a significant need for quality control in EBB. In pursuit of the long-term goal of flaw-free EBB, the objective of this dissertation is three-fold; 1) quality control through a bottom-up optimization approach, 2) defect detection via in-situ sensing and machine learning, and 3) preemptive process control via machine learning print quality prediction using process parameter inputs. In pursuit of objective 1, the optimization approach yielded significant defects, stressing the need for in-situ monitoring. In the pursuit of objectives 2 and 3, models predicted print quality features with accuracy nearing 90%, and strand width heterogeneity was significantly reduced to approximately 50 µm difference between strand halves.
Gerdes, Samuel, "Defect Detection and Process Control in Biological Additive Manufacturing" (2022). ETD collection for University of Nebraska - Lincoln. AAI29321412.