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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This is a material driven study that explores how post-processing of a 3D-printed biocomposite may increase its utility in the public furniture industry. The study thereby aims to contribute insights in material development and inspire a shift in practices that pushes the industry towards a more sustainable design process. By studying theories on sustainable development, biocomposites, and additive manufacturing, the surface defects in large-scale 3D-printing are put in relation to the industry-specific requirements placed on public furnishings. The potentials for the biocomposite to satisfy these demands are assessed using the four actions steps of material driven design. This includes hands-on exploration of several post-processing methods to minimize the material’s distinctive surface roughness. The most effective surface treatment, a combination of subtractive and additive processing, is subsequently applied in a product development phase to exemplify the feasibility of these methods in the context of furniture. This resulted in a design concept which, although a time-consuming process, proves the possibility of post-processing to influence the ability of the material to meet the requirements for public use. The increased material utility achieved in this study should, however, be considered relative to the economic and ecological consequenses associated with biocomposite processing.