Mechanical strength of polylactic acid (PLA) is increasingly relevant with time because of its attractive mechanical properties and 3D printability. Additive manufacturing (AM) methods, such as fused deposition modeling (FDM), stereolithography (SLA), and selective laser sintering (SLS), serve a vital role in assisting designers with cheap and efficient generation of the desired components. This document presents research to investigate the anisotropic response of multi-oriented PLA subjected to multiple monotonic loading conditions. Although empirical data has previously been captured for multi-oriented PLA under tensile and compressive loading conditions, the data has yet to be applied with regard to a representative component geometry. The tensile and compressive empirical data were ultimately used to develop elastic and yield constitutive models which aided in the characterization of PLA under torsion and bending. This representative component geometry is expected to experience a combined torsion and bending load condition in an effort to address this integral gap in the mechanical properties of multi-oriented PLA. In addition to the acquired empirical data, finite element analysis (FEA) and analytical modeling are employed to supplement the accurate modeling of future component analysis. As a result of the proposed array of experiments, the torsional and bending capabilities of PLA are forecasted to vary based on the print orientation. Lastly, the broader impact of this work is dedicated to addressing the material's capability to operate in environments which possess significant torsion and bending such as model aircraft wings and shafts for remote controlled cars.

This study proposed a utility-driven two-stage stochastic mixed-integer linear programming model to understand how the patient preferences impact the additive manufacturing (AM) supply chain design decisions. The goal of the mathematical model is to maximize the utilities derived from the customer preferences by appropriately allocating the AM facilities in the targeted region under customer decision and demand uncertainty. The mathematical model is visualized and validated by developing a real-life case study that utilizes the biomedical implants data for the state of Mississippi. A number of sensitivity analyses are conducted to understand how the patients' behavioral decisions (e.g., price-centric versus time- or quality-centric customers) to purchase biomedical implants impact the AM supply chain design decisions. The results revealed key managerial insights that could be utilized by healthcare service providers and interested stakeholders to provide quality healthcare services by managing patient-centric AM facility siting decisions.

The objective of this dissertation is to provide key methodological advancements towards the use of transfer learning in Laser-Based Additive Manufacturing (LBAM), to assist practitioners in producing high-quality repeatable parts. Currently, in LBAM processes, there is an urgent need to improve the quality and repeatability of the manufacturing process. Fabricating parts using LBAM is often expensive, due to the high cost of materials, the skilled machine operators needed for operation, and the long build times needed to fabricate parts. Additionally, monitoring the LBAM process is expensive, due to the highly specialized infrared sensors needed to monitor the thermal evolution of the part. These factors lead to a key challenge of improving the quality of additively manufactured parts, because additional experiments and/or sensors is expensive. We propose to use transfer learning, which is a statistical technique for transferring knowledge from one domain to a similar, yet distinct, domain, to leverage previous non-identical experiments to assist practitioners in expediting part certification. By using transfer learning, previous experiments completed in similar, but non-identical, domains can be used to provide insight towards the fabrication of high-quality parts. In this dissertation, transfer learning is applied to four key domains within LBAM. First, transfer learning is used for sensor fusion, specifically to calibrate the infrared camera with true temperature measurements from the pyrometer. Second, a Bayesian transfer learning approach is developed to transfer knowledge across different material systems, by modelling material differences as a lurking variable. Third, a Bayesian transfer learning approach for predicting distortion is developed to transfer knowledge from a baseline machine system to a new machine system, by modelling machine differences as a lurking variable. Finally, compensation plans are developed from the transfer learning models to assist practitioners in improving the quality of parts using previous experiments. The work of this dissertation provides current practitioners with methods for sensor fusion, material/machine calibration, and efficient learning of compensation plans with few samples.

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