The thesis presents an Additive Manufacturing Enabling Technology for Optically Transparent Glass.The platform builds on existing manufacturing traditions and introduces new dimensions of novelty across scales by producing unique structures with numerous potential applications in product-, and architectural-design. The platform is comprised of scalable modular elements able to operate at the high temperatures required to process glass from a molten state to an annealed product. The process demonstrated enables the construction of 3D parts as described by Computer Aided Design (CAD) models. Processing parameters such as temperature, flow rate, layer height and feed rate, can be adjusted to tailor the printing process to the desired component; its shape and its properties. The research explores, defines and hard-codes geometric constraints and coiling patterns as well as the integration of various colors into the current controllable process, contributing to a new design and manufacturing space. Performed characterization of the printed material to determine its morphological, mechanical and optical properties, is presented and discussed. Printed parts demonstrated strong adhesion between layers and satisfying optical clarity. The molten glass 3D printer as well as the fabricated objects exhibited, demonstrate the production of parts which are highly repeatable, enable light transmission, and resemble the visual and mechanical performance of glass constructs that are conventionally obtained. Utilizing the optical nature of glass, complex caustic patterns were created by projecting light through the printed objects. The 3D printed glass objects and process described here, aim to contribute new capabilities to the ever-evolving history of a very challenging but limitless material -glass.

We present a data-driven approach for the creation of high-resolution, geometrically complex, and materially heterogeneous 3D printed objects at product scale. Titled Data-driven Material Modeling (DdMM), this approach utilizes external and user-generated data sets for the evaluation of heterogeneous material distributions during slice generation, thereby enabling the production of voxel-matrices describing material distributions for bitmap-printing at the 3D printer's native voxel resolution. A bitmap-slicing framework designed to inform material property distribution in concert with slice generation is demonstrated. In contrast to existing approaches, this framework emphasizes the ability to integrate multiple geometry-based data sources to achieve high levels of control for applications in a wide variety of design scenarios. As a proof of concept, we present a case study for DdMM using functional advection, and we demonstrate how multiple data sources used by the slicing framework are implemented to control material property distributions.

Despite significant advances in synthetic biology at industrial scales, digital fabrication challenges have, to date, precluded its implementation at the product scale. We present, Mushtari, a multimaterial 3D printed fluidic wearable designed to culture microbial communities. Thereby we introduce a computational design environment for additive manufacturing of geometrically complex and materially heterogeneous fluidic channels. We demonstrate how controlled variation of geometrical and optical properties at high spatial resolution can be achieved through a combination of computational growth modeling and multimaterial bitmap printing. Furthermore, we present the implementation, characterization, and evaluation of support methods for creating product-scale fluidics. Finally, we explore the cytotoxicity of 3D printed materials in culture studies with the model microorganisms, Escherichia coli and Bacillus subtilis. The results point toward design possibilities that lie at the intersection of computational design, additive manufacturing, and synthetic biology, with the ultimate goal of imparting biological functionality to 3D printed products.