Additive manufacturing (AM) technology is a key emerging field transforming how customized products with complex shapes are manufactured. AM is the process of layering materials to produce objects from three-dimensional (3D) models. AM technology can be used to print objects with complicated geometries and a broad range of material properties. However, the issue of ensuring the quality of printed products during the process remains an obstacle to industry-level adoption. Furthermore, the characteristics of AM processes typically involve complex process dynamics and interactions between machine parameters and desired qualities. The issues associated with quality assurance in AM processes underscore the need for research into smart quality assurance systems. To study the complex physics behind process interaction challenges in AM processes, this dissertation proposes the development of a data-driven smart quality assurance framework that incorporates in-process sensing and machine learning-based modeling by correlating the relationships among parameters, signatures, and quality. High-fidelity AM simulation data and the increasing use of sensors in AM processes help simulate and monitor the occurrence of defects during a process and open doors for data-driven approaches such as machine learning to make inferences about quality and predict possible failure consequences. To address the research gaps associated with quality assurance for AM processes, this dissertation proposes several data-driven approaches based on the design of experiments (DoE), forward prediction modeling, and an inverse design methodology. The proposed approaches were validated for AM processes such as fused filament fabrication (FFF) using polymer and hydrogel materials and laser powder bed fusion (LPBF) using common metal materials. The following three novel smart quality assurance systems based on a parameter–signature–quality (PSQ) framework are proposed: 1. A customized in-process sensing platform with a DOE-based process optimization approach was proposed to learn and optimize the relationships among process parameters, process signatures, and parts quality during bioprinting processes. This approach was applied to layer porosity quantification and quality assurance for polymer and hydrogel scaffold printing using an FFF process. 2. A data-driven surrogate model that can be informed using high-fidelity physical-based modeling was proposed to develop a parameter–signature–quality framework for the forward prediction problem of estimating the quality of metal additive-printed parts. The framework was applied to residual stress prediction for metal parts based on process parameters and thermal history with reheating effects simulated for the LPBF process. 3. Deep-ensemble-based neural networks with active learning for predicting and recommending a set of optimal process parameter values were developed to optimize optimal process parameter values for achieving the inverse design of desired mechanical responses of final built parts in metal AM processes with fewer training samples. The methodology was applied to metal AM process simulation in which the optimal process parameter values of multiple desired mechanical responses are recommended based on a smaller number of simulation samples. / Doctor of Philosophy / Additive manufacturing (AM) is the process of layering materials to produce objects from three-dimensional (3D) models. AM technology can be used to print objects with complicated geometries and a broad range of material properties. However, the issue of ensuring the quality of printed products during the process remains a challenge to industry-level adoption. Furthermore, the characteristics of AM processes typically involve complex process dynamics and interactions between machine parameters and the desired quality. The issues associated with quality assurance in AM processes underscore the need for research into smart quality assurance systems. To study the complex physics behind process interaction challenges in AM processes, this dissertation proposes a data-driven smart quality assurance framework that incorporates in-process sensing and machine-learning-based modeling by correlating the relationships among process parameters, sensor signatures, and parts quality. Several data-driven approaches based on the design of experiments (DoE), forward prediction modeling, and an inverse design methodology are proposed to address the research gaps associated with implementing a smart quality assurance system for AM processes. The proposed parameter–signature–quality (PSQ) framework was validated using bioprinting and metal AM processes for printing with polymer, hydrogel, and metal materials.

Vascular tissue engineering (VTE) with the use of polymeric scaffolds offers the potential to generate small-diameter (<6 mm) arteries. In this thesis, a degradable polar hydrophobic ionic (D-PHI) polyurethane porous scaffold was synthesized with the objective of demonstrating its potential application for VTE. D-PHI scaffold synthesis was optimized, maximizing isocyanate and methacrylate monomer conversion. Through the incorporation of a lysine-based crosslinker, scaffold mechanical properties and swelling were manipulated. Furthermore, D-PHI scaffolds demonstrated the ability to support the growth and adhesion of A10 vascular smooth muscle cells (VSMCs) during two weeks of culture. This study also investigated the effect of a double porogen approach on D-PHI scaffold properties, demonstrating an increase in the total scaffold porosity and pore interconnectivity. Specifically, it was found that the use of 10 wt% polyethylene glycol and 65 wt% sodium bicarbonate porogens resulted in a porous (79±3%) D-PHI scaffold with the mechanical properties (elastic modulus=0.16±0.03 MPa, elongation-at-yield=31±5%, and tensile strength=0.04±0.01 MPa) required to withstand the physiologically-relevant cyclic mechanical strain (CMS) that is experienced by VSMCs in vivo. Furthermore, the effects of uniaxial CMS (10% strain, 1 Hz, 4 weeks) on human coronary artery smooth muscle cells (hCASMCs), which were cultured in a porous D-PHI scaffold, were studied using a customized bioreactor. Four weeks of CMS was shown to yield greater DNA mass, more cell area coverage, a better distribution of cells within the scaffold, the maintenance of contractile protein expression and the improvement of tensile mechanical properties. The in vitro and in vivo degradation as well as the in vivo biocompatibility of D-PHI scaffolds were also investigated. Following their subcutaneous implantation in rats (100 days), porous D-PHI scaffolds demonstrated more cell/tissue infiltration within their pores and degraded in a controlled manner and at a faster rate when compared to in vitro studies (120 days), retaining the mechanical integrity required during neo-tissue formation. This thesis provides significant insight into the role of the D-PHI scaffold in combination with physiologically-relevant CMS in modulating VSMC proliferation and phenotype. The findings of this work can be used to tailor vascular tissue regeneration by regulating VSMC function in a directed manner.

Vascular tissue engineering (VTE) with the use of polymeric scaffolds offers the potential to generate small-diameter (<6 mm) arteries. In this thesis, a degradable polar hydrophobic ionic (D-PHI) polyurethane porous scaffold was synthesized with the objective of demonstrating its potential application for VTE. D-PHI scaffold synthesis was optimized, maximizing isocyanate and methacrylate monomer conversion. Through the incorporation of a lysine-based crosslinker, scaffold mechanical properties and swelling were manipulated. Furthermore, D-PHI scaffolds demonstrated the ability to support the growth and adhesion of A10 vascular smooth muscle cells (VSMCs) during two weeks of culture. This study also investigated the effect of a double porogen approach on D-PHI scaffold properties, demonstrating an increase in the total scaffold porosity and pore interconnectivity. Specifically, it was found that the use of 10 wt% polyethylene glycol and 65 wt% sodium bicarbonate porogens resulted in a porous (79±3%) D-PHI scaffold with the mechanical properties (elastic modulus=0.16±0.03 MPa, elongation-at-yield=31±5%, and tensile strength=0.04±0.01 MPa) required to withstand the physiologically-relevant cyclic mechanical strain (CMS) that is experienced by VSMCs in vivo. Furthermore, the effects of uniaxial CMS (10% strain, 1 Hz, 4 weeks) on human coronary artery smooth muscle cells (hCASMCs), which were cultured in a porous D-PHI scaffold, were studied using a customized bioreactor. Four weeks of CMS was shown to yield greater DNA mass, more cell area coverage, a better distribution of cells within the scaffold, the maintenance of contractile protein expression and the improvement of tensile mechanical properties. The in vitro and in vivo degradation as well as the in vivo biocompatibility of D-PHI scaffolds were also investigated. Following their subcutaneous implantation in rats (100 days), porous D-PHI scaffolds demonstrated more cell/tissue infiltration within their pores and degraded in a controlled manner and at a faster rate when compared to in vitro studies (120 days), retaining the mechanical integrity required during neo-tissue formation. This thesis provides significant insight into the role of the D-PHI scaffold in combination with physiologically-relevant CMS in modulating VSMC proliferation and phenotype. The findings of this work can be used to tailor vascular tissue regeneration by regulating VSMC function in a directed manner.