Additive manufacturing (AM) – colloquially known as “3D Printing” – has proven to be a transformative technology by enabling fabrication of complex structures previously unachievable by conventional means. Direct ink writing (DIW) AM is a technology with massive potential to advance manufacturing due to its theoretically vast breadth of printable materials. However, since its introduction over two decades ago, adoption of DIW has lagged behind other AM techniques, with its use still predominantly relegated to research laboratories. The research presented in this dissertation aims to broaden the application spaces of DIW am through intertwined advancement of AM build hardware, materials, and control schemes in addition to incorporation of machine learning. In other words, “what” can be printed, and “how” can it be printed? Novel printable materials were developed including rapidly solidifying photopolymers, high-strength dual network thermoset polymers, conductive composites, and heteropolymer resins with application spaces including aerospace, biomedical, automotive, renewable energy, construction, and robotics. These materials were printed using custom DIW techniques designed to address applications such as facile manufacture of woven carbon fiber-reinforced composites; or to fabricate new components directly onto existing structures, which can have unstructured surfaces unsuitable for conventional DIW 3D printing. Combining these efforts enables fabrication of complex functional structures, with a myriad of capabilities including in-situ deformation monitoring, heating elements, and integrated circuitry. The focus of this dissertation lies at the intersection of additive manufacturing and materials development combined with artificial intelligence to enable next-generation manufacturing.
This will be held as a hybrid event: