Nanomaterials are becoming increasingly prevalent in final use products as we continue to improve our understanding of their structure and properties and optimize their processing. The useful applications for these materials extend from new drug delivery systems to improved materials for various transport industries and many more. Nanoscale materials which are commonly used include but are not limited to carbon nanotubes, graphene, silica, nanoclays, and cellulose nanocrystals. The literature presented herein aims to investigate structure-process-property relationships of cellulose nanocrystal (CNC) polymer composites. These CNC nanocomposites are unique in that they provide a dynamic mechanical response when exposed to H2O. Currently, these nanocomposite systems are most commonly solvent cast into their final geometry. In order to enable the use of these materials in more commercial processing methods such as extrusion, we must understand their inherent structure-process-property relationships. To do this, we first characterize the influence of temperature and shear orientation on the unique mechanical adaptive response. Next, the melt processability of the nanocomposite was characterized using material extrusion (MatEx) additive manufacturing (AM). Additionally, the diffusion behavior of water within the film, which controls the dynamic mechanical response, was probed to better predict the concentration dependent behavior. Finally, a literature review is presented which outlines the state of the art for melt extrusion AM of fiber filled polymer composite materials and provides insight into how we can further improve mechanical properties through further addition of composite filler materials. The initial focus of the dissertation is on the influence of melt processing CNC thermoplastic urethane (TPU) composites and the resulting impact on the mechanical adaptive response. Dynamic mechanical analysis (DMA) fitted with a submersion clamp was used to measure the mechanical softening of the composite while submerged in water. Small angle x-ray scattering (SAXS) and polarized raman spectroscopy were used to qualify the orientation of the various CNC/TPU composite samples. The results of the orientation measurements show that solvent casting the films orient CNCs into a mostly random state and melt extrusion induces some degree of uniaxial orientation. The DMA results indicate that at the processing conditions tested, the uniaxial orientation and thermal exposure from the melt processing do not significantly impact the mechanical responsiveness of the material. The next objective of this work was to expand upon the aforementioned learnings and determine the CNC composite material processability using MatEx. The ability to process mechanically dynamic CNC/TPU composites with a selective deposition process capable of generating complex geometries may enable new functionality and design freedom. To realize this potential, a two factor (extrusion temperature and extrusion speed) three level (240, 250 and 260 ℃/ 600, 1100 and 1600 mm/min) design of experiments (DOE) was utilized. The resulting printed parts were characterized by DMA to determine their respective mechanical adaptivity. Processing conditions did prove to have a significant impact on the mechanical adaptivity of the printed part. A correlation between applied energy and mechanical adaptivity demonstrates how increasing residence time and temperature can reduce mechanical performance. The shape fixity of the printed parts was calculated to be 80.4% and shape recovery was 44.2%. A 3D prototype part was also produced to demonstrate the unique properties of this material. Although the understanding of the melt processing behavior of these CNC composites had been improved, a stronger understanding of the moisture diffusion behavior within the composite is required to fully realize and control their potential. Therefore, a study was undertaken to capture the diffusion behavior and correlate it to the mechanical responsive mechanism. To do this, a thermogravimetric sorption analysis (TGA-SA) instrument was used to monitor the mass uptake as a function of time exposed to a humid environment. These data were then compared to DMA data collected for the same samples exposed to a similar degree of humidity. All studies were conducted as a function of concentration in order to better elucidate the influence that percolating network structures may have on the resultant properties. Interestingly, the results show how increasing addition of CNCs results in a decrease in the rate of diffusivity, which is counter to what has been commonly hypothesized. It is hypothesized that increasing CNC content restricts the mobility of surrounding amorphous matrix material, thus increasing the resistance for diffusion of a water molecule. However, the rate of mechanical adaptation was found to increase with increasing CNC content, which is believed to be a result of the increased connectivity, enabling further transport of water molecules. / Doctor of Philosophy / Nanomaterials are becoming increasingly prevalent in final use products as we continue to improve our understanding of their structure and properties and optimize their processing. The useful applications for these materials extend from new drug delivery systems to improved materials for various transport industries and many more. The literature presented herein aims to investigate structure-process-property relationships of cellulose nanocrystal (CNC) polymer composites. These CNC nanocomposites are unique in that they provide a unique mechanical response when exposed to water. In order to enable the use of these materials in more commercial processing methods, we must understand their inherent structure-process-property relationships. The following documents multiple aspects of these unique composite materials which enables their commercial viability and scientific versatility.

Vat photopolymerization's (VP) use in large-scale industrial manufacturing is limited due to poor scalability, and limited catalogue of engineering polymers. The challenges in scalability stem from an inherent process paradox: the feature resolution, part size, and manufacturing throughput cannot be maximized simultaneously in standard VP platforms. In addition, VP's inability to process viscous and high-molecular weight engineering polymers limits the VP materials catalogue. To address these limitations, the research presented in this work was conducted in two stages: (1) Development and modeling of new VP platforms to address the scalability and viscosity challenges, and (2) Investigating the influence of using the new processes on the cured polymer network structure and mechanical properties. First, a scanning mask projection vat photopolymerization (S-MPVP) system was developed to address the scalability limitations in VP systems. The process paradox was resolved by scanning the mask projection device across the resin surface while simultaneously projecting the layer as a movie. Using actual projected pixel irradiance distribution, a process model was developed to capture the interaction between projected pixels and the resin, and predict the resulting cure profile with an error of 2.9%. The S-MPVP model was then extended for processing heterogeneous UV scattering resins (i.e. UV curable polymer colloids). Using computer vision, the scattering of incident UV radiation on the resin surface was successfully captured and used to predict scattering-compensated printing parameters (bitmap pattern, exposure time , scanning speed). The developed reverse-curing model was used to successfully fabricate complex features using photocurable SBR latex with XY errors < 1.3%. To address the low manufacturing throughput of VP systems, a recoat-less, volumetric curing VP system that fabricates parts by continuously irradiating the resin surface with a movie composed of different gray-scaled bitmap images ( Free-surface movie mask projection (FreeMMaP)) was developed. The effect of cumulative exposure on the cure profile (X,Y,Z dimensions) was investigated and used to develop an iterative gray-scaling algorithm that generated a combination of gray-scaled bitmap images and exposure times that result in accurate volumetric curing (errors in XY plane and Z axis < 5% and 3% respectively). Results of this work demonstrate that the elimination of the recoating process increased manufacturing speed by 8.05 times and enabled high-resolution fabrication with highly viscous resins or soft gels. Then, highly viscous resins were made processible in VP systems by using elevated processing temperatures to lower resin viscosity. New characterization techniques were developed to determine the threshold printing temperature and time that prevented the onset of thermally-induced polymerization. The effect of printing temperature on curing, cured polymer structure, cured polymer mechanical properties, and printable aspect ratio was also investigated using diacrylate and dimethacrylate resins. Results of this investigation revealed increasing printing temperature resulted in improvements in crosslink density, tensile strength, and printability. However, presence of hydroxl groups on the resin backbone caused deterioration of crosslink density, mechanical properties, and curing properties at elevated printing temperatures. Finally, the lack of a systematic, constraint based approach to resin design was bridged by using the results of earlier process-structure-property explorations to create an intuitive framework for resin screening and design. Key screening parameters (such as UV absorptivity, plateau storage modulus) and design parameters (such as photoinitiator concentration, polymer concentration, UV blocker concentration) were identified and the methods to optimize them to meet the desired printability metrics were demonstrated using case studies. Most work in vat photopolymerization either deal with materials development or process development and modeling. This dissertation is placed at the intersection of process development and materials development, thus giving it an unique perspective for exploring the inter-dependency of machine and material. The process models, machines and techniques used in this work to make a material printable will serve as a guide for chemists and engineers working on the next generation of vat photopolymerization machines and materials. / Doctor of Philosophy / Vat Photopolymerization (VP) is a polymer-based additive manufacturing platform that uses UV light to cure a photo-sensitive polymer into the desired shape. While parts fabricated via VP exhibit excellent surface finish and high-feature resolution, their use for commercial manufacturing is limited because of its poor scalability for large-scale manufacturing and limited selection of engineering materials. This work focuses on the development of new VP platforms, process models and the investigation of the process-structure-property relationships to mitigate these limitations and enable fabrication of performance polymers. The first section of the dissertation presents the development of two new VP platforms to address the limitations in scalability. The Scanning Mask Projection Vat Photopolymerization (S-MPVP)) was developed to fabricate large area parts with high-resolution features and the Free-surface movie mask projection (FreeMMaP) VP platform was developed to enable high-speed, recoat-less, volumetric fabrication of 3D objects. Computer-vision based models were developed to investigate the influence of these new processes on the resultant cure shape and dimensional accuracy. Process models that can: (1) predict the cure profile for given input printing parameters (error < 3%), (2) predict the printing parameters (exposure time, bitmap gray-scaling) required for accurate part fabrication in homogeneous and UV scattering resins, and (3) generate gray-scaled bitmap images that can induce volumetric curing inside the resin (dimensional accuracy of 97% Z axis, 95% XY axis), were designed and demonstrated successfully. In the second portion of this work, the use of high-temperature VP to enable processing of high-viscosity resins and expansion of materials catalogue is presented. New methods to characterize the resin's thermal stability are developed. Techniques to determine the printing temperature and time that will prevent the occurrence of thermally-induced polymerization are demonstrated. Parts were fabricated at different printing temperatures and the influence of printing temperature on the resultant mechanical properties and polymer network structure was studied. Results of this work indicate that elevated printing temperature can be used to alter the final mechanical properties of the printed part and improve the printability of the high-resolution, slender features. Finally, the results of the process-structure-property investigations conducted in this work were used to guide the development of a resin design framework that highlights the parameters, metrics, and methods required to (1) identify printable resin formulations, and (2) tune printable formulations for optimal photocuring. Elements of this framework were then combined into an intuitive flowchart to serve as a design tool for chemists and engineers.

Metal additive manufacturing stands poised to disrupt multiple industries with high material use efficiency and complex part production capabilities, however many technologies deposit material with sub-optimal properties, limiting their use. This decrease in performance largely stems from porosity laden parts, and asymmetric solidification-based microstructures. Solid-state additive manufacturing techniques bypass these flaws, using deformation and diffusion phenomena to bond material together layer by layer. Among these techniques, Additive Friction Stir Deposition (AFSD), stands out as unique for its freeform nature, and thermomechanical conditions during material processing. Leveraging its solid-state behavior, optimized microstructures produced by AFSD can reach performance levels near, at, or even above traditionally prepared metals. A strong understanding of the material conditions during AFSD and the phenomena responsible for microstructure evolution. Here we discuss two works aimed at improving the state of knowledge surrounding AFSD, promoting future microstructure optimization. First, a parametric study is performed, finding a wide array of producible microstructures across two material systems. In the second work, a stop-action type experiment is employed to observe the dynamic microstructure evolution across the AFSD material flow pathway, finding specific thermomechanical regimes that occur within. Finally, multiple conventional alloy systems are discussed as their microstructure evolution pertains to AFSD, as well as some more unique systems previously limited to small lab scale techniques, but now producible in bulk due to the additive nature of AFSD. / Doctor of Philosophy / The microstructure of a material describes the atomic behavior at multiple length scales. In metals this microstructure generally revolves around the behavior of millions of individual crystals of metal combined to form the bulk material. The state and behavior of these crystals and the atoms that make them up influence the strength and usability of the material and can be observed using various high fidelity characterization techniques. In metal additive manufacturing (i.e. 3D printing) the microstructure experiences rapid and severe changes which can alter the final properties of the material, typical to a detrimental effect. Given the other benefits of additive manufacturing such as reduced costs and complex part creation, there is desire to predict and control the microstructure evolution to maximize the usability of printed material. Here, the microstructure evolution in a solid-state metal additive manufacturing, Additive Friction Stir Deposition (AFSD), is investigated for different metal material systems. The solid-state nature of AFSD means no melting of the metal occurs during processing, with deformation forcing material together layer by layer. The conditions experienced by the material during printing are in a thermomechanical regime, with both heating and deformation applied, akin to common blacksmithing. In this work specific microstructure evolution phenomena are discussed for multiple materials, highlighting how AFSD processing can be adjusted to change the resulting microstructure and properties. Additionally, specific AFSD process interactions are studied and described to provide better insight into cumulative microstructure evolution throughout the process. This work provides the groundwork for investigating microstructure evolution in AFSD, as well as evidence and results for a number of popular metal systems.

The research presented in this dissertation supports the overall goal of producing sensor functionalized neural microphysiological systems to enable deeper fundamental understandings of disease pathology and to provide drug screening and discovery platforms for improved clinical translation. Towards this goal, work addressing three broad objectives has been completed. The first objective was expanding the manufacturing process capabilities for hydrogels and tissues through augmentation of the 3D printing systems and developing novel modeling capabilities. The second objective was to expand the palette of available materials which exhibit the rheological properties required for 3D printing and the mechanical and biological properties required for neural tissue culture. The third objective was to develop sensing capabilities for both monitoring and control of the manufacturing process and to provide non-destructive assessment of microphysiological systems in real-time to quantify the dynamics of disease progression or response to treatment. The first objective of process improvement was addressed both through modification of the 3D printing system itself and through modeling of process physics. A new manifold was implemented which enabled on-the-fly mixing of bioprinting inks (bioinks) to produce smooth concentration gradients or discrete changes in concentration. Modeling capabilities to understand the transport occurring during both the processing and post-processing windows were developed to provide insight into the relationship between the programmed concentration distribution and its temporal evolution and stability. Vacuum-based pick-and-place capabilities for integration of prefabricated components for sensing and stimulation into the printed hydrogel constructs were developed. Models of the stress profiles, which relate to cell viability, within the printing nozzle during extrusion were produced using parameters extracted from rheological characterization of bioinks. The second objective was addressed through the development hydrogel bioinks which exhibited yield stresses without the use of rheological modifiers (fillers) to enable 3D printing of free-standing neural tissue constructs. A hybrid bioink was developed using the combination of a synthetic polaxamer with biomacromolecules present in native neural tissue. Functionalization of the biomacromolecules with catechol or methacrylate groups enabled two crosslinking mechanisms: chelation and UV exposure. Crosslinked gels exhibited moduli in the range of native neural tissue and enabled high viability culture of multiple neural cell types. The third objective was addressed through the characterization and implementation of physical and electronic sensors. The resonance of millimeter-scale dynamic-mode piezoelectric cantilevers submerged in polymer solutions was found to persist into the gel phase enabling viscoelastic sensing in hydrogels and monitoring of sol-gel transitions. Resonant frequency and quality factor of the cantilevers were related with the viscoelastic properties of hydrogels through both a first principles approach and empirical correlation. Electrode functionalized hollow fibers were implemented as impedimetric sensors to monitor bioink quality during 3D printing. Impedance spectra were collected during extrusion of cell-laden bioinks and the magnitude and phased angle of the impedance response correlated with quality measures such as cell viability, cell type, and stemness which were validated with traditional off-line assays. / Doctor of Philosophy / The research presented in this dissertation supports the overall goal of producing sensor functionalized neural microphysiological systems to enable deeper fundamental understandings of disease pathology and to provide drug screening and discovery platforms for improved clinical translation. Microphysiological systems are miniaturized tissue constructs which strive to mimic the complex conditions present in-vivo within an in-vitro platform. By producing these microphysiological systems with sensing functionality, new insight into the mechanistic progression of diseases and the response to new treatment options can be realized. Towards this goal, work addressing three broad objectives has been completed. The first objective was expanding the manufacturing process capabilities for hydrogels and tissues through augmentation of the 3D printing systems and developing novel modeling capabilities. The second objective was to expand the palette of available materials which exhibit both the properties required for 3D printing and the mechanical and biological properties required for neural tissue culture. The third objective was to develop sensing capabilities for both monitoring and control of the manufacturing process and to provide non-destructive assessment of microphysiological systems in real-time to quantify the dynamics of disease progression or response to treatment. Through these efforts higher quality microphysiological systems may be produced benefitting future researchers, medical professionals, and patients.

Additive manufacturing (AM) is a growing section of the advanced manufacturing field and is being used to fabricate an increasing number of critical components, from aerospace components to medical implants. At the same time, cyber-physical attacks targeting manufacturing systems have continued to rise. For this reason, there is a need to research new techniques and methods to ensure the integrity of parts fabricated on AM systems. This work seeks to address this need by first performing a detailed analysis of vulnerabilities in the AM process chain and how these attack vectors could be used to execute malicious part sabotage attacks. This work demonstrated the ability of an internal void attack on the .STL file to reduce the yield load of a tensile specimen by 14% while escaping detection by operators. To mitigate these vulnerabilities, a new impedance-based approach for in situ monitoring of AM systems was created. Two techniques for implementing this approach were investigated, direct embedding of sensors in AM parts, and the use of an instrumented fixture as a build plate. The ability to detect changes in material as small as 1.38% of the printed volume (53.8 mm3) on a material jetting system was demonstrated. For metal laser powder bed fusion systems, a new method was created for representing side-channel meltpool emissions. This method reduces the quantity of data while remaining sensitive enough to detect changes to the toolpath and process parameters caused by malicious attacks. To enable the SCMS to validate part quality during fabrication required a way to receive baseline part quality information across an air-gap. To accomplish this a new process noise tolerant method of cyber-physical hashing for continuous data sets was presented. This method was coupled with new techniques for the storage, transmission, and reconstructing of the baseline quality data was implemented using stacks of "ghost" QR codes stored in the toolpath to transmit information through the laser position. A technique for storing and transmitting quality information in the toolpath files of parts using acoustic emissions was investigated. The ATTACH (additive toolpath transmission of acoustic cyber-physical hash) method used speed modulation of infill roads in a material extrusion system to generate acoustic tones containing quality information about the part. These modulations were able to be inserted without affecting the build time or requiring additional material and did not affect the quality of the part that contained them. Finally, a framework for the design and implementation of a SCMS for protecting AM systems against malicious cyber-physical part sabotage attacks was created. The IDEAS (Identify, Define, Establish, Aggregate, Secure) framework provides a detailed reference for engineers to use to secure AM systems by leveraging the previous work in vulnerability assessment, creation of new side-channel monitoring techniques, concisely representing quality data, and securely transmitting information to air-gapped systems through physical emissions. / Doctor of Philosophy / Additive manufacturing (AM), more widely known as 3D printing, is a growing field of manufacturing where parts are fabricated by building layers of material on top of each other. This layer-based approach allows the production of parts with complex shapes that cannot be made using more traditional approaches such as machining. This capability allows for great freedom in designing parts, but also means that defects can be created inside of parts during fabrication. This work investigates ways that an adversary might seek to sabotage AM parts through a cyber-physical attack. To prevent attacks seeking to sabotage AM parts several new approaches for security are presented. The first approach uses tiny vibrations to detect changes to part shape or material by attaching a small sensor either directly to the parts or to the surface that they are built on. Because an attack that sabotages an AM system (3D printer) could also affect the systems used to detect part defects these systems should be digitally separated from each other. By using a series of QR codes fabricated by the AM system along with the parts, information can be sent from the AM system to the monitoring system through its sensors. This prevents a cyber-attack from jumping from the AM system to the monitoring system. By temporarily turning off the laser power and tracking the movements of the guiding mirrors the QR code information can be sent to the monitoring system without having to actually print the QR code. The information stored in the QR code is compared to the emission generated when fabricating the parts and is used to detect if an attack has occurred since that would change the emissions from the part, but not from the QR code. Another approach for sending information from the AM system using physical emissions is by using sounds generated during part fabrication. Using a desktop scale 3D printer, the speed of certain movements was increased or decreased. The change in speed causes the sound emitted from the printer to change, while not affecting the actual quality of the print. By using a series of tones, similar to Morse code, information can be sent from the printer. Research was performed on the best settings to use to transmit the information as well as how to automatically receive and decode the information using a microphone. The final step in this work is a framework that serves as a guide for designing and implementing monitoring systems that can detect sabotage attacks on AM parts. The framework covers how to evaluate a system for potential vulnerabilities and how to use this information to choose sensors and data processing techniques to reduce the risk of cyber-physical attacks.

Additive friction stir deposition (AFSD) is an emerging solid-state metal additive manufacturing technology that uses deformation bonding to create near-net shape 3D components. As a developing technology, a deeper understanding of the processing science is necessary to establish the process-structure relationships and enable improved control of the as-printed microstructure and material properties. AFSD provides a unique opportunity to explore the friction stir fundamentals via direct observation of the material during processing. This work explores the relationship between the processing parameters (e.g., tool rotation rate Ω, tool velocity V, and material feed rate F) and the thermomechanical history of the material by process monitoring of i) the temperature evolution, ii) the force evolution, and iii) the interfacial contact state between the tool and deposited material. Empirical trends are established for the peak temperature with respect to the processing conditions for Cu and Al-Mg-Si, but a key difference is noted in the form of the power law relationship: Ω/V for Cu and Ω2/V for Al-Mg-Si. Similarly, the normal force Fz for both materials correlates to V and inversely with Ω. For Cu both parameters show comparable influence on the normal force, whereas Ω is more impactful than V for Al-Mg-Si. On the other hand, the torque Mz trends for Al-Mg-Si are consistent with the normal force trends, however for Cu there is no direct correlation between the processing parameters and the torque. These distinct relationships and thermomechanical histories are directly linked to the contact states observed during deformation monitoring of the two material systems. In Cu, the interfacial contact between the material and tool head is characterized by a full slipping condition (δ=1). In this case, interfacial friction is the dominant heat generation mechanism and compression is the primary deformation mechanism. In Al-Mg-Si, the interfacial contact is characterized by a partial slipping/sticking condition (0<δ<1), so both interfacial friction and plastic energy dissipation are important mechanisms for heat generation and material deformation. Finally, an investigation into the contact evolution at different processing parameters shows that the fraction of sticking is critically dependent on the processing parameters which has many implications on the thermomechanical processing history. / Doctor of Philosophy / Additive manufacturing or three-dimensional (3D) printing technologies have been lauded for their ability to fabricate complex geometries and multi-material parts with reduced material waste. Of particular interest is the use of metal additive manufacturing for repair and fabrication of industrial and structural components. This work focuses on characterizing the thermomechanical processing history for a developing technology Additive Friction Stir Deposition (AFSD). AFSD is solid-state additive manufacturing technology that uses frictional heat and mechanical mixing to fabricate 3D metal components. From a fundamental materials science perspective, it is imperative to understand the processing history of a material to be able to predict the performance and properties of a manufactured part. Through the use of infrared imaging, thermocouples, force sensors, and video monitoring this work is able to establish quantitative relationships between the equipment processing parameters and the processing history for Cu and Al. This work shows that there is a fundamental difference in how these two materials are processed during AFSD. In the future, these quantitative relationships can be used to validate modeling efforts and improve manufacturing quality of parts produced via friction stir techniques.

Although additive manufacturing technologies are often referred to as "3D printing," the family of technologies typically deposit material on a layer-by-layer basis. For material extrusion (ME) in particular, the deposition process results in weak inter- and intra-layer bonds that reduce mechanical performance in those directions. Despite this shortcoming, ME offers the opportunity to specifically and preferentially align the reinforcement of a composite material throughout a part by customizing the toolpath. Recent developments in multi-axis deposition have demonstrated the ability to place material outside of the XY-plane, enabling depositions to align to any 3D (i.e., non-planar) vector. Although mechanical property improvements have been demonstrated, toolpath planning capabilities are limited; the geometries and load paths are restricted to surface-based structures, rather than fully 3D load paths. By specifically planning deposition paths (roads) where the composite reinforcement is aligned to the load paths within a structure, there is an opportunity for a step-change in the mechanical properties of ME parts. To achieve this goal for arbitrary geometries and load paths, the author presents a design and process planning workflow that concurrently optimizes the topology of the part and the toolpath used to fabricate it. The workflow i) identifies the optimal structure and road directions using topology optimization (TO), ii) plans roads aligned to those optimal directions, iii) orders those roads for collision-free deposition, and iv) translates that ordered set of roads to a robot-interpretable toolpath. A TO algorithm, capable of optimizing 3D material orientations, is presented and demonstrated in the context of 2D and 3D load cases. The algorithm achieved a 38% improvement in final solution compliance for a 3D Wheel problem relative to existing TO algorithms with planar orientation optimization considerations. Optimized geometries and their associated orientation fields were then propagated with the presented alignment-focused deposition path planner and conventional toolpath planners. The presented method resulted in a 97% correlation between the road directions and the orientation field, while the conventional methods only achieved 77%. A planar multi-load case was then fabricated using each of these methods and tested in both tension and bending; the presented alignment-focused method resulted in a 108.24% and 29.25% improvement in each load case, respectively. To evaluate the workflow in a multi-axis context, an inverted Wheel problem was optimized and processed by the workflow. The resulting toolpaths were then fabricated on a multi-axis deposition platform and mechanically evaluated relative to geometrically similar structures using a conventional toolpath planner. While the alignment in the multi-axis specimen was improved from the conventional method, the mechanical properties were reduced due to limitations of the multi-axis deposition platform. / Doctor of Philosophy / The material extrusion additive manufacturing process is widely used by hobbyists and industry professionals to produce demonstration parts, but the process is often overlooked for end-use, load bearing parts. This is due to the layer-by-layer fabrication method used to create the desired geometries; the bonding between layers is weaker than the direction material is deposited. If load paths acting on the printed structure travel across those layer interfaces, the part performance will decrease. Whereas gantry-based systems are forced into this layer-by-layer strategy, robotic arms allow the deposition head to rotate, which enables depositions to be placed outside of the XY-plane (i.e., the typical layer). If depositions are appropriately planned using this flexibility, the layer interfaces can be oriented away from the load paths such that all of the load acts on the (stronger) depositions. Although this benefit has been demonstrated in literature, the existing methods for planning robotic toolpaths have limits on printability; certain load paths and geometries cannot be printed due to concerns that the robotic system will collide with the part being printed. This work focuses on increasing the generality of these toolpath planning methods by enabling any geometry and set of load paths to be printed. This is achieved through three objectives: i) identify the load paths within the structure, ii) plan roads aligned to those load paths, iii) order those roads such that collisions will not occur. The author presents and evaluates a design workflow that addresses each of these three objectives by simultaneously optimizing the geometry of the part as well as the toolpath used to fabricate it. Planar and 3D load cases are optimized, processed using the presented workflow, and then fabricated on a multi-axis deposition platform. The resulting specimens are then mechanically tested and compared to specimens fabricated using conventional toolpath planning methods.

Additive manufacturing provides unmatched control and diversity over structural design of polymeric, ceramic and metallic parts. Nevertheless, until recently, the toolbox of polymeric feedstocks for light based additive manufacturing limited employment of printed parts for applications necessitating high thermomechanical performance. Development of synthetic pathways permitted the first additive manufacturing of high performance poly(amide imides) via ultraviolet assisted direct ink write (UV-DIW) printing. Precursor resins exhibited prerequisite rheology and reactivity for UV-DIW and produced organogels were well-defined and self-supporting. Thermal treatment induced drying and imidization of the precursor organogels to form the desired poly(amide imide) structures. During post-processing the parts displayed linear isotropic shrinkage as low as 26% and exhibited competitive thermomechanical properties. Following expansion of the high performance backbones available for additive manufacturing, simplification of synthetic rigors was undertaken. This investigation facilitated the evolution of the first photocurable and processable small molecule polyimide precursors. These supramolecular carboxylate ammonium nylon salts, coined polysalts, allowed for additive manufacturing of both high performance polyimides and polyetherimides using vat photopolymerization (VP). The use of small molecule precursors over previously investigated polymeric precursors displayed much lower solution viscosities yielding reduction of organic solvent loading, inducing lower overall shrinkage. Polysalts provide a stimulating platform for rapid and facile printing of high performance polyimides in the future. Surveying the excellent carbonization behavior for aromatic polyimides spurred translation of known 2D protocols to post-processing of printed polyimides. Applying pyrolysis methodologies to parts produced using VP and UV-DIW induced efficient carbonization at 1000 °C. Remarkably, the carbonized parts retained structure and did not display cracks or pore formation. Raman spectroscopy indicated production of disordered carbon via the utilized pyrolysis protocol, in line with literature on carbonization of PMDA-ODA polyimide at 1000 °C. Electrical testing indicated production of conductive materials following pyrolysis, with carbonization temperature modulating the performance. The excellent thermal stability, transport properties, and known mechanical performance of carbonaceous materials may enable application of these printed objects in customized electronics and aerospace environments. Exploration of drop-in monomeric units permitted a multi-pronged research program into augmentation of mechanical, rheological and transport properties of high performance polyetherimides (PEIs). Installation of sodium or lithium substituted disulfonated monomers via classical two-step polyimide synthesis afforded two series of sulfonated polyetherimides (sPEI). The sPEIs exhibited robust thermal properties, with high sulfonate mol% inducing Tg > 300 °C. X-ray scattering experiments revealed the development of domains via inclusion of the sulfonate moieties, with low mol% producing larger domain spacing. The larger domains present in the low mol% sPEIs yielded improved ionic liquid uptake within 2 d, yielding improved ionic conductivities at room temperature relative to high mol% samples. The observed conductivities indicated potential of the sPEIs as battery electrolytes, but further ionic liquid incorporation is required for competitive performance. Development of a poly(ethylene glycol) (PEG) bearing macromonomer facilitated synthesis of PEIs and PI graft copolymers. When coupled with 4,4'-(4,4'-isopropylidene-diphenoxy)diphthalic anhydride (BPADA) and meta-phenylene diamine (mPD), the PEG-grafted materials exhibited signs of phase mixing at low mol% incorporation of macromonomer, with a single observable Tg depressed from neat BPADA-mPD. Doping of the PEI-g-PEG with lithium salts allowed for production of polymeric films that displayed good ionic conductivities at room temperatures. Extension of the PEG macromonomer into fully aromatic PIs yielded phase separated materials even at modest loadings, >2.5 mol%. The formed PEG-g-PMDA-ODA contained thermally stable PI main-chains with thermally labile graft chains, which when thermally treated induced facile quantitative PEG removal. Remarkably, the thermally treated materials retained flexibility, even at >60 wt.% PEG removal. Further investigations aim to explore use of novel PEIs in energy storage as well as low density and dielectric materials. / Doctor of Philosophy / High performance polymers enjoy wide use in microelectronics and aerospace industries due to high thermal stability and excellent mechanical performance. However, processing restrictions hinder manufacturing of 3-dimensional objects of many high performance polymers suitable for extreme environments. Additive manufacturing, also known as 3D printing, has garnered attention in both academic and industrial settings over the last four decades due to the unmatched control over part design and internal structure, but the material arsenal for additive manufacturing of polymers lacks options for applications demanding high thermal stability. The first half of this dissertation aimed to promote translation of high performance polymeric chemistries to suitable feedstocks for additive manufacturing. By designing and developing novel chemical pathways, traditional processing limitations were circumvented and high performance polymers, such as poly(amide imides) and polyimides, were successfully processed via light based additive manufacturing. Likewise, by investigating carbonization dynamics of polyimides and expanding current additive manufacturing techniques for processing of fully aromatic polyimides, complex 3D carbonaceous materials were obtained. These carbon objects present extreme thermal stability and electrical conductivity, advantageous for aerospace and electronic industries. Additionally, investigations allowed for development of synthetically facile routes for expanding the available polyimide backbones for additive manufacturing via use of small molecule precursors. The second half of the dissertation explored novel polyetherimide and polyimide reagents for production of functional materials. Harnessing ionic building blocks permitted synthesis of a series of thermally robust polyetherimides displaying promise for energy storage. Similarly, coupling previous literature for ion conduction in solid polymer electrolytes for battery applications with thermally stable and flame resistant polyetherimides enabled synthesis of a series of innovative graft copolymers with good room temperature ionic conductivities. Lastly, pairing of thermally labile polymers with thermally resistant polyimide backbones allowed for development of an exciting platform for obtaining highly insulting and flexible films for electronics applications. Outlined future work aims to probe the formation of pores in the obtained polymer

Additive manufacturing (AM) is a powerful emerging technology for fabricating components with complex geometries using a variety of materials. However, despite the promising potential, due to the complexity of the process dynamics, how to ensure product quality and consistency of AM parts efficiently during the process remains challenging. Therefore, this dissertation aims to develop advanced machine learning methods for online process monitoring and quality assurance of smart additive manufacturing. Driven by edge computing, the Industrial Internet of Things (IIoT), sensors and other smart technologies, data collection, communication, analytics, and control are infiltrating every aspect of manufacturing. The data provides excellent opportunities to improve and revolutionize manufacturing for both quality and productivity. Despite the massive volume of data generated during a very short time, approximately 90 percent of data gets wasted or unused. The goal of sensing and data analytics for advanced manufacturing is to capture the full insight that data and analytics can discover to help address the most pressing problems. To achieve the above goal, several data-driven approaches have been developed in this dissertation to achieve effective data preprocessing, feature extraction, and inverse design. We also develop related theories for these data-driven approaches to guarantee their performance. The performances have been validated using sensor data from AM processes. Specifically, four new methodologies are proposed and implemented as listed below: 1. To make the unqualified thermal data meet the spatial and temporal resolution requirement of microstructure prediction, a super resolution for multi-sources image stream data using smooth and sparse tensor completion is proposed and applied to data acquisition of additive manufacturing. The qualified thermal data is able to extract useful information like boundary velocity, thermal gradient, etc. 2. To effectively extract features for high dimensional data with limited samples, a clustered discriminant regression is created for classification problems in healthcare and additive manufacturing. The proposed feature extraction method together with classic classifiers can achieve better classification performance than the convolutional neural network for image classification. 3. To extract the melt pool information from the processed X-ray video in metal AM process, a smooth sparse Robust Tensor Decomposition model is devised to decompose the data into the static background, smooth foreground, and noise, respectively. The proposed method exhibits superior performance in extracting the melt pool information on X-ray data. 4. To learn the material property for different printing settings, a multi-task Gaussian process upper confidence bound is developed for the sequential experiment design, where a no-regret algorithm is implemented. The proposed algorithm aims to learn the optimal material property for different printing settings. By fully utilizing the sensor data with innovative data analytics, the above-proposed methodologies are used to perform interdisciplinary research, promote technical innovations, and achieve balanced theoretical/practical advancements. In addition, these methodologies are inherently integrated into a generic framework. Thus, they can be easily extended to other manufacturing processes, systems, or even other application areas such as healthcare systems. / Doctor of Philosophy / Additive manufacturing (AM) technology is rapidly changing the industry, and data from various sensors and simulation software can further improve AM product quality. The objective of this dissertation is to develop methodologies for process monitoring and quality assurance using advanced data analytics. In this dissertation, four new methodologies are developed to address the problems of unqualified data, high dimensional data with limited samples, and inverse design. Related theories are also studied to identify the conditions by which the performance of the developed methodologies can be guaranteed. To validate the effectiveness and efficiency of proposed methodologies, various data sets from sensors and simulation software are used for testing and validation. The results demonstrate that the proposed methods are promising for different AM applications. The future applications of the accomplished work in this dissertation are not just limited to AM. The developed methodologies can be easily transferred for applications in other domains such as healthcare, computer vision, etc.

Filament-based material extrusion (MatEx) additive manufacturing has garnered huge interest in both academic and industrial communities. Moreover, there is an increasing need to expand the material catalog for MatEx to produce end use parts for a wide variety of functional applications. Current approaches towards MatEx of semicrystalline thermoplastics are in their nascent stage with fiber reinforcements being one of the most common techniques. MatEx of commodity semicrystalline thermoplastics has been investigated but most of the current methods are extremely material and machine specific. The goal of this dissertation is to enable MatEx of semicrystalline polymers with mechanical properties approaching that of injection molded parts. Tailored molecular architectures of blends that can control the crystallization kinetics from the melt state are investigated. By modifying the crystallization time window, the time during which chain diffusion can occur across the deposited layers is prolonged, which allows for a stronger bond between layers. Such differences in the crystallization process impact the z-axis adhesion and residual stress state, which directly affect mechanical properties and warpage in the printed parts. The impact of blend composition on polymer chain diffusion, crystallization profiles, and print properties resulting from the repeated non-uniform thermal history in filament based MatEx is studied. The melt flow behaviour is characterized using rheology and its effect on the interlayer adhesion of printed parts and print precision is explored. The extent of polymer chain re-entanglement post deposition on the printer bed is quantitatively determined using interrupted shear rheology protocols. Tensile bars are printed and mechanically characterized to analyze the tensile performance of the printed parts. Correlating the rheological findings with the mechanical performance of the printed parts provides valuable insights into the complex interlayer welding process during MatEx and is critical to improving existing machine designs and feedstocks in order to achieve printed parts with properties approaching their injection molded counterparts. The results will be essential in identifying optimal processing conditions to maximize material specific printed part performance as well as highlight the associated limitations to enable MatEx of next generation materials. / Doctor of Philosophy / Compared to traditional subtractive manufacturing techniques, additive manufacturing (AM) has the potential to transform modern manufacturing capabilities due to its unique advantages including design flexibility, mass customization, energy efficiency, and economic viability. The filament-based material extrusion (MatEx), also referred to as fused filament fabrication (FFF), employing thermoplastic polymers (and composites) has emerged as one of the most common AM modality for industrial adoption due to its operational simplicity. However, the widespread application of MatEx has been limited due to the lack of compatible materials, anisotropic mechanical properties, and lack of quality assurance. Most of the research on FFF has been performed on amorphous polymers with almost negligible levels of crystalline content such as polylactic acid (PLA) and acrylonitrile-butadiene-styrene (ABS). Semicrystalline polymers are an attractive choice for FFF feedstocks compared to the amorphous ones due to their improved thermal resistance, toughness, and deformability. However, processing semicrystalline polymers using FFF is challenging due to the volumetric shrinkage encountered during crystallization from the melt state. This results in the buildup of significant levels of residual stresses at temperatures lower than the crystallization temperature of the polymer resulting in warpage of the printed parts. The research presented in this dissertation aims to address the aforementioned challenges by characterizing semicrystalline polymer feedstocks under conditions representative of the multiphysics encountered during a typical FFF process. Several strategies to limit shrinkage and warpage are discussed that involve tuning the thermal profile and crystallization kinetics during printing. The former is achieved by addition of thermally conductive carbon fiber reinforcements while the latter is realized by blending amorphous resins or low crystallinity polymers to the semicrystalline polymer matrix. The fibers results in a more homogenous temperature distribution during printing while the incorporation of the resins modify the rate of crystallization; both of which play a pivotal role in reducing the residual stress build-up and hence minimizing the warpage during printing. The printability of the materials is investigated based on the shear- and temperature dependent viscous response of the polymers. The printed parts with fiber reinforcements exhibit high levels of mechanical anisotropy compared to the blends with the resins, likely due to differences in polymer chain mobility at the interface. The tensile properties of the printed polymer blends are slightly inferior to those obtained using traditional manufacturing techniques; however, properties close to 90-95% of injection molded properties are recovered through a simple post-processing thermal annealing step. The obtained results will assist in optimizing the processing parameters and feedstock formulation in order to consistently produce printed parts with minimal defects and tailored mechanical properties for functional applications.