Laser-based additive manufacturing is inherently associated with extreme, unprecedented, and rapid thermokinetics which impact the microstructural evolution in a built component. Such a unique, near to non-equilibrium microstructure/phase evolution in laser additively manufactured metallic components impact their properties in engineering application. In light of this, the present work investigates the unique microstructural traits as a result of process induced spatial and temporal variation in thermokinetic parameters in laser directed energy deposited CoCrMo biomedical alloy. The influence of such a unique microstructural evolution in laser directed energy deposited CoCrMo on electrochemical response in physiological media was elucidated and compared with a conventionally manufactured, commercially available CoCrMo component. Furthermore, while investigation of the electrochemical response, such a microstructural evolution in laser directed energy deposited CoCrMo led to in-situ surface modification of the built components in physiological media via selective, non-uniform electrochemical etching. Such in-situ surface modification resulted in enhanced biocompatibility in terms of mammalian cell growth, cell-substrate adhesion, blood compatibility, and antibacterial properties indicating improved osteointegration, compared to a conventionally manufactured, commercially available CoCrMo component.

Ceramic additive manufacturing is gaining popularity with methods like selective laser sintering (SLS), binder jetting, direct ink writing and stereolithography, despite their disadvantages. Laser sintering and binder jetting are too expensive, while direct ink writing lacks resolution and stereolithography lacks scalability. The project aims to combine one of the most versatile, affordable, and readily available 3D printing methods: fused filament fabrication (FFF) with polymer derived ceramics to produce cellular ceramics to overcome the disadvantages posed by the other methods. The process uses a two-step approach. The first step is to 3D print the part using a polymer FFF 3D printer with a thermoplastic polyurethane filament and the second step is to impregnate the part in a polysilazane preceramic polymer and then pyrolyze it in an inert environment up to 1200C. The resulting product is a high-resolution cellular ceramic of the composition SiOC(N). This type of cellular ceramic can find an application in several fields such as scaffolds for bone tissue regeneration, liquid metal filtering, chemical and gas filtering, catalytic converters and electric applications. The process can provide an affordable alternative to the products used in these fields currently.

Additive Manufacturing is a fast-growing new technology with the advantages of manufacturingvirtually any shape and low-cost when the production volume is low, it would be a good solutionfor scale aircraft model production, which has complex-shape parts and small-batch production.However, this industry has not yet introduced this technology. Through method of modeling, prototyping and comparing, this project investigates the AMtechniques and materials suitable for the production of scale aircraft models, and how to assignthem to specific parts, which is the path towards using Additive Manufacturing in scale aircraftmodel production. / Additiv tillverkning är en snabbväxande ny teknik med fördelarna av att tillverka praktiskt tagetvilken form som helst och till låg kostnad när produktionsvolymen är låg, det skulle vara en bralösning för tillverkning av skalenlig flygplansmodell, som har komplexa delar och små partierproduktion. Men denna industri har ännu inte introducerat denna teknik. Genom metoder för modellering, prototypframställning och jämförelse undersöker detta projektAM-tekniker och material som är lämpliga för produktion av skalenlig flygplansmodell, och hurman tilldelar dem till specifika delar, vilket är vägen mot att använda Additive Tillverkning iskalenlig flygplansmodellproduktion.

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Laser powder bed fusion (LPBF), one of the most promising additive manufacturing (AM) techniques, has enabled the production of previously impossible structures. This breakthrough in AM has not only facilitated the creation of new designs, but also the redesign of existing industrial and engineering components to produce lightweight and highly efficient dies and molds, biomaterial scaffolds, aircraft brackets, heat sink and heat exchangers. In many of the mentioned applications in industries such as automotive, aerospace, heat exchanger, and electronics, aluminum (Al), Al alloys, and Al matrix composites (AMCs) are considered potential candidates. In the first phase of this study, the optimum powder particle size and size distribution of an Al alloy powder (i.e., AlSi10Mg) was determined with the aim being to achieve highest densification levels and dimensional accuracies. In the second phase, three materials with high thermal conductivities were selected, namely, pure Al, AlSi12 and AlSi10Mg alloys. Since Al/Al alloys are prone to oxidation, the LPBF process parameters were optimized not only in terms of the densification level but also oxygen content of the fabricated parts. It was found out that the rate of oxide diminishment for Al/Al alloys during the LPBF process is more than in-situ oxidation. Despite the efforts, the optimized LPBF fabricated samples showed lower thermal conductivity than their conventionally manufactured counterparts. To tackle the issue, two different potential solutions were put into test. In the third phase, the influence of preheating on thermal properties of pure Al, AlSi12, and AlSi10Mg was investigated and a huge improvement in the thermal conductivity of the optimized as-built parts was obtained. In the fourth phase, the possibility of enhancing thermal conductivity of the LPBF fabricated Al/Al alloys in as-built condition through the incorporation of a second constituent with a higher thermal conductivity (i.e., graphene) was investigated. / Thesis / Doctor of Philosophy (PhD)

Laser powder bed fusion is one of the most effective ways to achieve metal additive manufacturing. However, this method still suffers from deformation, delamination, dimensional error, and porosities. One of the most significant issues is poor printing accuracy, especially for small features such as turbine blade tips. The main reason for the shape inaccuracy is the heat accumulation caused by using constant laser power regardless of the shape variations. Due to the highly complex and dynamic nature of the laser powder bed fusion, improving the printing quality is challenging. Research gaps exist from many perspectives. For example, the lack of understanding of multi-physical melt pool dynamics fundamentally impedes the research progress. The scarcity of a customizable laser powder bed platform further restricts the possibility of testing the improvement strategies. Additionally, most state-of-the-art quality inspection techniques suitable for laser powder bed fusion are costly in economic and time aspects. Lastly, the rapid and chaotic printing process is hard to monitor and control. This dissertation proposes a complete research scheme including a fundamental physics study, process signature and quality correlation, smart additive manufacturing platform development, high-performance sensor development, and a robust real-time closed-loop control system design to address all these issues. The entire research flow of this dissertation is as follows: 1. This work applies and integrates three advanced sensing technologies: synchrotron X-ray imaging, high-speed IR camera, and high-spatial-resolution IR camera to characterize the melt pool dynamics, keyhole, porosity formation, vapor plume, and thermal evolution in Ti-64 and 410 stainless steel. The study discovers a strong correlation between the thermal and X-ray data, enabling the feasibility of using relatively cheap IR cameras to predict features that can only be captured using costly synchrotron X-ray imaging. Such correlation is essential for thermal-based melt pool control. 2. A highly customizable smart laser powder bed fusion platform is designed and constructed. This platform integrates numerous sensors, including but not limited to co-axial cameras, IR cameras, oxygen sensors, photodiodes, etc. The platform allows in-process parameter adjusting, which opens the boundary to test various control theories based on multi-sensing and data correlations. 3. To fulfill the quality assessment need of laser powder bed fusion, this dissertation proposes a novel structured light 3D scanner with extraordinary high spatial resolution. The spatial resolution and accuracy are improved by establishing hardware selection criteria, integrating the proper hardware, designing a scale-appropriate calibration target, and developing noise reduction procedures during calibration. Compared to the commercial scanner, the proposed scanner improves the spatial resolution from 48 µm to 5 µm and the accuracy from 108.5 µm to 0.5 µm. 4. The final goal of quality improvement is achieved through designing and implementing a real-time closed-loop system into the smart laser powder bed fusion platform. The system regulates the laser power based on the monitoring result from a novel thermal sensor. The desired printing temperature is found by correlating the laser power, the dimensional accuracy, and the thermal signatures from a set of thin-wall structure printing trails. An innovative high-speed data acquisition and communication software can operate the whole system with a graphic user interface. The result shows the laser power can be successfully controlled with 2 kHz, and a significant improvement in small feature printing accuracy has been observed. / Doctor of Philosophy / Laser powder bed fusion is one of the most effective ways to achieve metal additive manufacturing. However, this method still suffers from defects such as deformation, delamination, dimensional error, and porosities. Due to the highly complex and dynamic nature of the laser powder bed fusion, improving the printing quality is challenging. Research gaps exist from many perspectives, such as the lack of understanding of melt pool dynamics; the scarcity of a customizable laser powder bed platform; the need for suitable sensors; and the missing of a control system that can effectively regulate the rapid and chaotic printing process. This dissertation proposes a complete research scheme to address all these issues. The fundamental study characterizes the melt pool dynamics and discovers a strong correlation between the melt pool thermal and geometrical data, enabling thermal-based melt pool control. Following that, a highly customizable smart laser powder bed fusion platform is designed and constructed. The platform allows in-process parameter changes, opening the boundary to test various control theories. A novel structured light 3D scanner with an ultra-high spatial resolution was proposed to fulfill the quality assessment need. The final goal of quality improvement is achieved through designing and implementing a real-time closed-loop system into the smart laser powder bed fusion platform. The system regulates the laser power based on real-time thermal monitoring. The result shows the laser power can be successfully controlled with 2 kHz, and a significant improvement in printing accuracy is achieved.

The reliance on low-viscosity and photoactive resins limits the accessible properties for vat photopolymerization (VP) materials required for engineering applications. This has limited the adoption of VP for producing end-use parts, which typically require high MW polymers and/or more stable chemical functionality. Decoupling the viscosity and molecular weight relationship for VP resins has been completed recently for polyimides and highperformance elastomers by photocuring a scaffold around polymer precursors or polymer nanoparticles, respectively. Both of these materials are first shaped by printing a green part followed by thermal post-processing to achieve the final part properties. This dissertation focuses on improving the processability of these material systems by (i) investigating the impact of scaffold architecture and polysalt monomer composition on photocuring, thermal post-processing, and resulting thermomechanical properties and (ii) developing a Monte Carlo ray-tracing (MCRT) simulation to predict light scattering and photocuring behavior in particle-filled resins, specifically zinc oxide nanoparticles in a rigid polyester resin and styrene butadiene rubber latex resin. The first portion of the dissertation introduces VP of a tetra-acid and half-ester-based polysalt resin derived from 4,4'-oxydiphthalic anhydride and 4,4-oxydianiline (ODPA-ODA), a fully aromatic polyimide with high glass transition temperature and thermal stability. This polyimide, and polyimides like this, find use in demanding industries such as aerospace, automotive and electronic applications. The author evaluated the hypothesis that a non-bound triethylene glycol dimethacrylate (TEGDMA) scaffold would facilitate more efficient scaffold burnout and thus achieve parts with reduced off-gassing potential at elevated temperatures. Both resins demonstrated photocuring and were able to print solid and complex latticed parts. When thermally processed to 400 oC, only 3% of the TEGDMA scaffold remained within the final parts. The half-ester resin exhibits higher char yield, resulting from partial degradation of the polyimide backbone, potentially caused by lack of solvent retention limiting the imidization conversion. The tetra-acid exhibits a Tg of 260oC, while the half-ester displays a higher Tg of 380 oC caused by the degradation of the polymer backbone, forming residual char, restricting chain mobility. Solid parts displayed a phase-separated morphology while the half-ester latticed parts appear solid, indicating solvent removal occurs faster in the half-ester composition, presumably due to reduced polar acid functionality. This platform and scaffold architecture enables a modular approach to produce novel and easily customizable UV-curable polyimides to easily increase the variety of polyimides and the accessible properties of printed polyimides through VP. The second section of this dissertation describes the creation and validation of a MCRT simulation to predict light scattering and the resulting photocured shape of a ZnO-filled resin nanocomposite. Relative to prior MCRT simulations in the literature, this approach requires only simple, easily acquired inputs gathered from dynamic light scattering, refractometry, UV-vis spectroscopy, beam profilometry, and VP working curves to produce 2D exposure distributions. The concentration of 20 nm ZnO varied from 1 to 5 vol% and was exposed to a 7X7 pixel square ( 250 µm) from 5 to 11 s. Compared to experimentally produced cure profiles, the MCRT simulation is shown to predict cure depth within 10% (15 µm) and cure widths within 30% (20 µm), below the controllable resolution of the printer. Despite this success, this study was limited to small particles and low loadings to avoid polycrystalline particles and maintain dispersion stability for the duration of the experiments. Expanding the MCRT simulation to latex-based resins which are comprised of polymer nanoparticles that are amorphous, homogeneous, and colloidally stable. This allows for validating the MCRT with larger particles (100 nm) at higher loadings. Simulated cure profiles of styrene-butadiene rubber (SBR) loadings from 5 vol% to 25 vol% predicted cure depths within 20% ( µm) and cure widths within 50% ( µm) of experimental values. The error observed within the latex-based resin is significantly higher than in the ZnO resin and potentially caused by the green part shrinking due to evaporation of the resin's water, which leads to errors when trying to experimentally measure the cure profiles. This dissertation demonstrates the development of novel and functional materials and creation process-related improvements. Specifically, this dissertation presents a materials platform for the future development of unique photocurable engineering polymers and a corresponding physics-based model to aid in processing. / Doctor of Philosophy / Vat Photopolymerization (VP) is a 3D printing process that uses ultraviolet (UV) light to selectively cure liquid photosensitive resin into a solid part in a layer-by-layer fashion. Parts produced with VP exhibit a smooth surface finish and fine features of less than 100 µm (i.e., width of human hair). Recoating the liquid resin for each layer limits VP to low-viscosity resins, thus limiting the molecular weight (and thus performance) of the printed polymers accessible. Materials that are low molecular weight are limited in achieving desirable properties, such as elongation, strength, and heat resistance. Solvent-based resins, such as polysalt and latex resins have demonstrated the ability to decouple the viscosity and molecular weight relationship by eliminating polymer entanglements using low-molecular-weight precursors or isolating high-molecular-weight polymers into particles. This dissertation focuses on expanding and improving the printability of these methods. The second chapter of the dissertation investigates the impact of scaffold architecture in printing polyimide polysalts to improve scaffold burnout. Polysalts are polymers that exist as dissolved salts in solution, with each monomer holding two electronic charges. When heated, the solvent evaporates and the monomers react to form a high molecular-weight polymer. While previous work featured a polysalt that was covalently bonded to the monomers, the polysalt in this work is made printable by co-dissolving a scaffold. The polysalt resins are photocured and thermally processed to polymerize and imidize into a high-molecular-weight polymer, while simultaneously pyrolyzing the scaffold. Using a co-dissolved scaffold allows the investigation of two different monomers of tetra-acid and half-ester functionality. The half-ester composition underwent degradation during heating, increasing the printed parts' glass transition or softening point. The scaffold had little impact on the polysalt polymerization or final part properties and was efficiently removed, with only 3% remaining in final parts. The composition and properties of the monomers selected played a bigger role due to partial degradation altering the properties of the final parts. Overall, this platform and scaffold architecture allows for a larger number of polyimides to be accessible and easily customizable for future VP demands. The third chapter describes the challenges of processing photocurable resins that contain particles due to the UV light scattering in the resin vat during printing. When the light from the printer hits a particle, it is scattered in all directions causing the layer shape to be distorted from the designed shape. To overcome this, a Monte Carlo ray-tracing (MCRT) simulation was developed to mimic light rays scattering within the resin vat. The simulation was validated by comparing simulation results against experiment trials of photocuring resins containing 20nm zinc oxide (ZnO) nanoparticles. The MCRT simulation predicted all the experimental cure depths within 10% (20 µm) and cured widths within 30% (15 µm) error. Despite the high accuracy, this study was limited to small particles and low concentrations. Simulating larger particles is difficult as the simulation assumes each particle to be uniform throughout its volume, which is atypical of large ceramic particles. The fourth chapter enables high particle volume loading by using a highly stretchable styrene-butadiene rubber (SBR) latex-based resin. Latex-based resins maintain low viscosity by separating large polymer chains into nano-particles that are noncrystalline and uniform. When the chains are separated, they cannot interact or entangle, keeping the viscosity low even at high concentrations (>30 vol%). Like the ZnO-filled resin, the latex resin is experimentally cured and the MCRT simulation predicts the resulting cure shape. The MCRT simulation predicted cure depths within 20% (100 µm) and over-cure widths within 50% (100 µm) of experimental values. This error is substantially higher than the ZnO work and is believed to be caused by the water evaporating from the cured resin resulting in inconsistent measurements of the cured dimensions.

The Automated Design and Manufacturing Laboratory (ADML) is an automated assembly line located in the Engineering Product and Innovation Center (EPIC) that serves as the lab component for the course ME345: Automation and Manufacturing Methods. Over the semester the students learn how to program each automated component of the system, including Computer Numerically Controlled (CNC) mills, Universal Robot's 6 axis robotic arm, cameras, and Programmable Logic Controllers (PLC). Students then learn how to integrate each component together to develop a completely automated manufacturing process using an in-house manufacturing execution software. This integrated system is then used by the students to automatically manufacture new products of their own design that provide a societal benefit. Since 2019 multiple undergraduate students have worked on augmenting the ADML's capability with printing electronics by implementing Direct Ink Writing (DIW) based 3D printing and vacuum based pick and place into the ADML's assembly robot. Using these new capabilities, students in the ME345 will be able to design and manufacture electronic circuits. Moreover, a graduate level course will be developed based on this new addition to the ADML. The aim of this Thesis is to continue the work of previous students by finalizing the hardware and software necessary for the pick and place of electronic components and developing a conductive ink for electrical wiring and interconnects. A three component ink comprised of silver flake and a copolymer solution of acrylates/polytrimethylsiloxymethacrylate in a isododecane solvent was developed. This ink is biocompatible so it can be used by students without any hazard concern. It also exhibits a high degree of adhesion to the high-density polyethylene (HDPE) stock parts currently used in the ADML to ensure strong bonding to the electrical components. The mixing process, ink ingredient concentrations, and print parameters (i.e., extrusion pressure, print speed, and nozzle standoff distance) were optimized for compatibility with DIW based 3D printing, consistent and clog-free extrusion throughout the printing process, print fidelity, and a high electrical conductivity within approximately 1-2 orders of magnitude of bulk silver. / 2025-05-24T00:00:00Z

Aerospace and aeronautics industries push forward the research to improve constantly the quality, safety, and cost of flights. The main ways of improving products are to create lighter and better components, under a highly controlled processing from the research and development to the production at a big scale.  To achieve those goals, new processes and new materials are constantly created by engineers. In this perspective, titanium alloys have been developed and studied as they provide good mechanical properties and low density. To reduce the production costs and waste due to machining, additive manufacturing has started to be implemented on the manufacturing chains. Titanium alloys seemed to react very well to additive manufacturing, but there are still some problematics that need to be answered.  The main problematic of this thesis comes from the process development cell. It has been stated that two different titanium-based alloys, Ti-6Al-4V and Ti-6Al-2Mo-4Zr-2Sn, showed the same behavior under additive manufacturing, which is not an evident statement. Therefore, the goal of this work will be to understand the behavior of those two alloys by simulations and use of key parameters in order to model the process in a proper and accurate way.

With the advancement of equipment and the development of technology, the manufacturing process is becoming more and more advanced. This appears as an advanced manufacturing process that uses innovative technology, including robotics, artificial intelligence, and autonomous systems. Additive manufacturing (AM), also known as 3D printing, is the representative advanced manufacturing technology that creates 3D geometries in a layer-by-layer fashion with various types of materials. However, quality assurance in the manufacturing process requires high expectations as the process develops. Therefore, the objective of this dissertation is to propose innovative methodologies for process monitoring and control to achieve quality assurance in advanced manufacturing. The development of sensor technologies and computational power offer process data, providing opportunities to achieve effective quality assurance through a machine learning approach. Hence, exploring the connections between sensor data and process quality using machine learning methodologies would be advantageous. Although this direction is promising, some constraints and complex process dynamics in the actual process hinder achieving quality assurance from the existing machine learning methods. To address these challenges, several machine learning approaches assisted by the physics knowledge obtained from the process have been proposed in this dissertation. These approaches are successfully validated by various manufacturing processes, including AM and multistage assembly processes. Specifically, three new methodologies are proposed and developed, as listed below. -To detect the process anomalies with imbalanced process data due to different ratios of occurrence between process states, a new Generative Adversarial Network (GAN)-based method is proposed. The proposed method jointly optimizes the GAN and classifier to augment realistic and state-distinguishable images to provide balanced data. Specifically, the method utilizes the knowledge and features of normal process data to generate effective abnormal process data. The benefits of the proposed approach have been confirmed in both polymer AM and metal AM processes. -To diagnose process faults with a limited number of sensors caused by the physical constraints in the multistage assembly process, a novel sparse Bayesian learning is proposed. The method is based on a practical assumption that it will likely have a few process faults (sparse). In addition, the temporal correlation of process faults and the prior knowledge of process faults are considered through the Bayesian framework. Based on the proposed method, process faults can be accurately identified with limited sensors. -To achieve online defect mitigation of new defects that occurred during the printing due to the complex process dynamics of the AM process, a novel Reinforcement Learning (RL)-based algorithm is proposed. The proposed method is to learn the machine parameter adjustment to mitigate the new defects during the printing. The method transfers knowledge learned from various sources in the AM process to RL. Therefore, with a theoretical guarantee, the proposed method learns the mitigation strategy with fewer training samples than traditional RL. By overcoming the challenges in the process, the above-proposed methodologies successfully achieve quality assurance in the advanced manufacturing process. Furthermore, the methods are not designed for the typical processes. Therefore, they can easily be applied to other domains, such as healthcare systems. / Doctor of Philosophy / The development of equipment and technologies has led to advanced manufacturing processes. Along with that, quality assurance in the manufacturing processes has become a very important issue. Therefore, the objective of this dissertation is to accomplish quality assurance by developing advanced machine learning approaches. In this dissertation, several advanced machine learning methodologies using the physics knowledge from the process are proposed. These methods overcome some constraints and complex process dynamics of the actual process that degrade the performance of existing machine learning methodologies in achieving quality assurance. To validate the effectiveness of the proposed methodologies, various advanced manufacturing processes, including additive manufacturing and multistage assembly processes, are utilized. The performance of the proposed methodologies provides superior results for achieving quality assurance in various scenarios compared to existing state-of-the-art machine learning methods. The applications of the achievements in this dissertation are not limited to the manufacturing process. Therefore, the proposed machine learning approaches can be further extended to other application areas, such as healthcare systems.