Additive manufacturing, or 3D printing, enables the direct fabrication of complex structures from digital models. Vat photopolymerization (VPP) offers high resolution by selectively curing liquid resins with light. Multi-material VPP extends this capability by switching resins with techniques like bath exchange and fluidic flushing. However, these methods require pausing the print, limiting speed and reinforcing the resolution–throughput trade-off. The layer-by-layer process also hinders smooth material transitions and weakens interfacial bonding, restricting the scalability and fidelity of multi-material VPP.This dissertation aims to advance multi-material additive manufacturing by developing a high-speed multi-material 3D printing platform that integrates varying compositions in a fully continuous fashion, named dynamic fluid-assisted micro-continuous liquid interface production (DF-μCLIP) system. By utilizing a thickness-controlled polymerization-free “dead zone”, liquid resins are continuously replenished through a resin bath integrated with dynamic fluidic channels and coordinated material delivery systems for continuous printing with on-the-fly material switching, achieving multi-material printing at speeds up to 90 mm/hour with a pixel resolution of 7.4 μm. This approach improves interfacial mechanical integrity by forming intermixed polymer networks across material boundaries, facilitating smooth transitions between distinct materials. Furthermore, dynamic control over resin composition allows the creation of truly continuous material gradients during the uninterrupted printing, overcoming the limitations of traditional discrete gradient methods.
To harness the extended potential of this printing capability, novel 3D printable functional materials were developed, including electroactive hydrogels (EAH) and self-healing (SH) hydrogels, each optimized for specific applications. The graded EAHs created with DF-μCLIP are designed to enhance soft robotics by incorporating distinct regions with tailored mechanical and actuation properties. By integrating rigid and responsive zones within structures, these graded EAHs enable controlled actuation with enhanced adaptability and precision. Furthermore, high-performance SH hydrogels were developed, achieving full autonomous SH efficiency through interpenetrating networks. This SH hydrogel also exhibits excellent ion conductivity, making it highly suitable for sensing applications. Through systematic optimization and assessment of performance, this dissertation provides a foundational approach for creating complex, application-specific multi-material structures, expanding the capabilities of 3D printing in diverse fields, from soft robotics and wearable devices to underwater manipulators and flexible sensors.

Copper has long-lastingly drawn attention owing to its high thermal and electrical conductivities, finding applications in microelectronics, automotive, and aerospace industries. As a nanoporous foam, additional size-dependent properties such as high-surface-area and low-temperature sinterability open additional doors in micro-batteries, catalysis, and energetics fields. However, its manufacturability in powder metallurgy based processes still faces significant restraints in scalability, throughput, and energy-efficiency. This work offers solutions in copper manufacturing at both nano- and micro-scales. Chapter 2 focuses on fabricating high-surface-area parts by exploiting nanoporous copper powders as feedstock in casting and additive manufacturing. The hypothesis is that their low-temperature sinterability, combined with their flowability as micron-sized powders, makes them perfect candidates for fabricating parts with tunable porosity. The underlying physical phenomena of thermal coarsening and material interdiffusion that make this possible are studied, along with the required decomposition of inevitably formed oxides. Chapter 3 investigates energy-efficiency in copper printing via Laser Powder Bed Fusion (LPBF). Copper laser welding presents excessive energy losses as a highly reflective metal (HRM) with total reflectance ≥95% near the infrared range (e.g., 1070 nm). Most prints compensate it by increasing laser power (e.g., 700W-2kW) and lowering scan speeds (i.e., ≤0.8 m/s), ever more hindering their energy efficiency and throughput. A novel laser-matter interaction mode is introduced, with enhanced absorption and low part density (i.e., ≤78% in relative density) for pure copper at scan speeds over 4 m/s. This is possible as the laser partially interacts with the uncoalesced region ahead of the coalesced melt pool at these speeds, promoting increased absorption through multiple laser reflections. To investigate this, in-situ imaging was combined with synchronous laser location tracking. At speeds ≥4 m/s at 400W, the distance between the laser centroid and the coalesced melt pool cusp becomes shorter than the laser spot’s radius. Competing physical timescales available and required for coalescing molten particles through analytical models are also discussed. Though porous, prints in this regime can be 1250% faster at 5.3% of the volumetric energy density used in literature for similar densities. This offers opportunities for fast and energy-efficient LPBF of other HRMs such as aluminum and silver.

Modern advancements in additive manufacturing (AM) have removed the traditional limitations of “design for manufacturing” and enabled the creation of complex components, optimized for a given task. This is most apparent in the field of architected cellular materials, which possess the ability to augment the bulk material properties of a structure through the intelligent design of the cellular topology. Because of this, AM cellular materials are of great interest to the aerospace, medical, energy, and automotive industries. However, there are two critical gaps in the field of AM cellular material which this research aims to address: fatigue life prediction and multi-physics optimization.

To address these gaps, a first–principles approach was employed as a central theme of this research to deconfound the fields of fatigue and optimization which traditionally rely heavily on empirical models and correlations. With this in mind, three research themes emerged. The first theme “experimental method for early detection of crack initiation” sought to answer the question: “Can detection of local plastic deformation be used for early prediction of crack initiation regions in an AM metallic cellular material?” This resulted in the development of a novel spatial-temporal method for early detection of crack region and orientation from thermographic images within the first 2% of total life of the specimen. The second theme focused on the development of a computational prediction algorithm for crack initiation life which sought to answer the second question: “What is an accurate method for predicting the low-cycle fatigue (LCF) life in an AM metallic cellular material?”. In pursuit of this goal, a computational method based on first principles of thermodynamics, validated experimentally, was developed and found to be capable of predicting the crack initiation life within 30% error with only bulk material properties. The third and final theme of this research was multi-physics design optimization to answer the question: “What is the most efficient way to design and optimize AM cellular materials to increase performance?”. The optimization methodology developed leverages first principles of entropy minimization to quantify losses in a complex system and systematically reduce them to improve the efficiency of the design.