VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.

Browsing by Author "Yu, Hang"

Now showing 1 - 20 of 25
  • Thumbnail Image
    Additive Friction Stir Deposition of Aerospace Al-Zn-Mg-Cu-Zr Alloys: Leveraging Processing and Metallurgical Science for Structural Repair
    Hahn, Gregory David (Virginia Tech, 2024-02-05)
    Additive Friction Stir Deposition is an emerging solid-state additive manufacturing process that leverages severe plastic deformation to deposit fully dense metallic parts. This is of particular interest for high-strength aluminum alloys in which the addition of copper to the alloy chemistry makes them susceptible to hot cracking. This plagues traditional 3D printing of metals which is based on melting and solidification. This work looks at a particular high-strength aluminum alloy AA7050, one of the most widely utilized alloys for complex aerostructures. One of the key traits allowing for its widespread use is its low quench sensitivity, which enables it to be formed into thick sections and still achieve adequate strength. This work studies the feasibility of printing AA7050 and achieving full strength in thin cross sections as well as the influence of the zirconium dispersoid particle on quench sensitivity when applied to thicker sections. It was found that AA7050 after AFSD has significantly more quench sensitivity than traditionally processed material and through STEM, it was determined that this was due to the Al3Zr dispersoid particles providing heterogeneous precipitation sites. It was demonstrated that removing Zr alleviates the quench sensitivity in the case of printing with a featureless tool; however, the breakup of large constituent particles with a protrusion tool increases the number of interfaces for heterogeneous nucleation that induces sensitivity. This work shows that the dynamic recrystallization necessary to deposit material is detrimental to the fundamental performance of the alloy, making it challenging for thick AA7050 to achieve peak strength. A separate study is shown in which AFSD was utilized to successfully repair analogous corroded fastener holes in AA7050 commonly observed in service. After repairing with AFSD, the AA7050 outperformed the baseline material in R=0.1 and R=-1 fatigue, even outperforming pristine material in the R=0.1 case. This was determined to be due to the breakup of Fe-rich constituent particles serving as fatigue crack initiation sites which effectively delays the crack initiation process.
  • Thumbnail Image
    The Applicability of Additive Friction Stir Deposition for Bridge Repair
    Asiatico, Patricia Magistrado (Virginia Tech, 2021-06-07)
    The purpose of this research was to investigate the potential application of additive friction stir deposition (AFSD) to repair corroded steel bridge members. AFSD is an emerging solid-state additive manufacturing (AM) technology with many advantageous qualities such as low porosity, low residual stresses, flexibility in material, and a high build rate allowing for large-scale deposits. Two parameters were studied to understand the quality of AFSD on corroded steel: surface roughness and surface cleanliness. Three rounds of depositions were done: AerMet100, a high-strength corrosion-resistant steel, deposited onto AISI 1018 plates, with varying degrees of section loss, sectioned from a bridge taken out-of-service; AISI 1018 steel deposited onto an A572 Gr. 50 plate with 12 holes of varying diameters and depths drilled into the plate to simulate surface roughness; and AISI 1018 steel deposited onto an A572 Gr. 50 plate with mill scale, corrosion, and an industrial three-coat bridge paint system. The repair quality of each deposition was studied using scanning electron microscopy, microhardness testing, and three-point bending. Results from these tests indicated the following: AFSD can sufficiently mix dissimilar steels and result in a fine-grained microstructure; depositing onto a rough surface appeared to aid bonding between the two materials with little to no adverse effects on the repair quality; and finally, depending on the chosen deposition parameters, AFSD can mix foreign surface material into the matrix or mechanically remove the bulk of the foreign surface material appearing to clean the surface during the deposition.
  • Thumbnail Image
    Biomineralized Composites: Material Design Strategies at Building-Block and Composite Levels
    Deng, Zhifei (Virginia Tech, 2023-01-12)
    Biomineral composites, consisting of intercrystalline organics and biogenic minerals, have evolved unique structural designs to fulfill mechanical and other biological functionalities. Aside from the intricate architectures at the composite level and 3D assemblies of the biomineral building blocks, the individual mineral blocks enclose intracrystalline structural features that contribute to the strengthening and toughening at the intrinsic material level. Therefore, the design strategies of biomineralized composites can be categorized into two structural levels, the individual building block level and the composite level, respectively. This dissertation aims at revealing the material design strategies at both levels for the bioinspired designs of advanced structural ceramics. At the building block level, there is a lack of comparative quantification of the mechanical properties between geological and biogenic minerals. Correspondingly, I first benchmark the mechanical property difference between biogenic and geological calcite through nanoindentation techniques. The selected biogenic calcite includes Atrina rigida prisms and Placuna placenta laths, corresponding to calcite {0001}, and {101 ̅8} planes. The natural cleavage plane {101 ̅4} of geological calcite was added to the comparative study. Under indentation load, geological calcite deforms plastically via twinning and slips under low loads, and shifts to cleavage fracture under high loads. In comparison, the P. placenta composites, composed of micro-sized single-crystal laths and extensive intercrystalline organic interfaces, exhibit better crack resistance. In contrast, the single-crystal A. rigida prisms show brittle fracture with no obvious plastic deformation. Secondly, how the internal microstructures and loading types affect the mechanical properties of individual building blocks is investigated. The prismatic building blocks are obtained from the bivalves A. rigida and Sinanodonta woodiana, where the former consists of single-crystal calcite and the latter consists of polycrystalline aragonite. The comparative investigation under different loading conditions is conducted through micro-bending and nanoindentation. The continuous mineral matrix in A. rigida prisms leads to comparable modulus under tensile and compressive loadings in the elastic regime, while the high-density intracrystalline nanoinclusions contribute to the conchoidal fracture behaviors (instead of brittle cleavage). In comparison, the interlocking grain boundaries in S. woodiana prisms correlate with easier tensile deformation (smaller tensile modulus) than compression, as well as the intergranular fracture morphologies. The third topic in the biomineral-level investigation focuses on how biomineral utilizes residual stress at the macroscopic scale. The selected model system is the spine from the sea urchin Heterocentrotus mamillatus, which has a bicontinuous porous structure and mesocrystalline texture. It is confirmed that the spine has a macroscopic stress field with residual tension in the central medulla and compression in the radiating layers. The multimodal characterizations on the spine conclude that the structural origins are not associated with the gradient distribution of the intracrystalline defects, including Mg substitution in the calcite matrix, intracrystalline organics, and amorphous calcium carbonates (ACC). It is hypothesized that the residual stress is generated due to the volume expansion during ACC crystallization at the compacted growth front. At the composite level, even though enhanced crack resistance is expected in biomineralized composites due to their hierarchical structures, the correlation between their 3D composite structures and damage/crack evolution is quite limited in the literature. I developed in-situ testing devices integrated with synchrotron-based X-ray tomography to capture the crack propagation in the materials, including the four-point bending and compression/indentation configurations. Two representative models are chosen to demonstrate the deformation of biomineralized composites under bending and compression, respectively, including the calcium carbonate-based gastropod shell (Melo diadema) and the hydroxyapatite-based fish teeth (Pogonias cromis). Also, the two composites are designed to achieve different functional requirements, i.e., enhanced fracture toughness vs. wear resistance. The comprehensive characterizations of these two composites revealed how biological structural composites are designed accordingly to their functional needs. For the crossed-lamellar M. diadema shell, directional dependence of the shell property was revealed, where the transversal direction (perpendicular to the growth line) represents both the stronger and tougher direction, but the longitudinal direction is more resistant to notches and defects. For the P. cromis teeth, the enhanced wear resistance of the near-surface enameloid originates from the intricate designs at the microscale, with c-axes of hydroxyapatite crystals and micro-sized enameloid rods coaligned with biting direction and F and Zn doping. In addition, the fracture morphologies of the fish teeth correlate with the microstructures; the enameloid exhibits corrugated fracture paths due to the interwoven fibrous building blocks, and the dentin exhibits clean planar fracture surfaces.
  • Thumbnail Image
    Comparative Genomics and Proteomic Analysis of Assimilatory Sulfate Reduction Pathways in Anaerobic Methanotrophic Archaea
    Yu, Hang; Susanti, Dwi; McGlynn, Shawn E.; Skennerton, Connor T.; Chourey, Karuna; Iyer, Ramsunder; Scheller, Silvan; Tavormina, Patricia L.; Hettich, Robert L.; Mukhopadhyay, Biswarup; Orphan, Victoria J. (Frontiers, 2018-12-03)
    Sulfate is the predominant electron acceptor for anaerobic oxidation of methane (AOM) in marine sediments. This process is carried out by a syntrophic consortium of anaerobic methanotrophic archaea (ANME) and sulfate reducing bacteria (SRB) through an energy conservation mechanism that is still poorly understood. It was previously hypothesized that ANME alone could couple methane oxidation to dissimilatory sulfate reduction, but a genetic and biochemical basis for this proposal has not been identified. Using comparative genomic and phylogenetic analyses, we found the genetic capacity in ANME and related methanogenic archaea for sulfate reduction, including sulfate adenylyltransferase, APS kinase, APS/PAPS reductase and two different sulfite reductases. Based on characterized homologs and the lack of associated energy conserving complexes, the sulfate reduction pathways in ANME are likely used for assimilation but not dissimilation of sulfate. Environmental metaproteomic analysis confirmed the expression of 6 proteins in the sulfate assimilation pathway of ANME. The highest expressed proteins related to sulfate assimilation were two sulfite reductases, namely assimilatory-type low-molecular-weight sulfite reductase (alSir) and a divergent group of coenzyme F-420-dependent sulfite reductase (Group II Fsr). In methane seep sediment microcosm experiments, however, sulfite and zero-valent sulfur amendments were inhibitory to ANME-2a/2c while growth in their syntrophic SRB partner was not observed. Combined with our genomic and metaproteomic results, the passage of sulfur species by ANME as metabolic intermediates for their SRB partners is unlikely. Instead, our findings point to a possible niche for ANME to assimilate inorganic sulfur compounds more oxidized than sulfide in anoxic marine environments.
  • Thumbnail Image
    Data-driven Methods in Mechanical Model Calibration and Prediction for Mesostructured Materials
    Kim, Jee Yun (Virginia Tech, 2018-10-01)
    Mesoscale design involving control of material distribution pattern can create a statistically heterogeneous material system, which has shown increased adaptability to complex mechanical environments involving highly non-uniform stress fields. Advances in multi-material additive manufacturing can aid in this mesoscale design, providing voxel level control of material property. This vast freedom in design space also unlocks possibilities within optimization of the material distribution pattern. The optimization problem can be divided into a forward problem focusing on accurate predication and an inverse problem focusing on efficient search of the optimal design. In the forward problem, the physical behavior of the material can be modeled based on fundamental mechanics laws and simulated through finite element analysis (FEA). A major limitation in modeling is the unknown parameters in constitutive equations that describe the constituent materials; determining these parameters via conventional single material testing has been proven to be insufficient, which necessitates novel and effective approaches of calibration. A calibration framework based in Bayesian inference, which integrates data from simulations and physical experiments, has been applied to a study involving a mesostructured material fabricated by fused deposition modeling. Calibration results provide insights on what values these parameters converge to as well as which material parameters the model output has the largest dependence on while accounting for sources of uncertainty introduced during the modeling process. Additionally, this statistical formulation is able to provide quick predictions of the physical system by implementing a surrogate and discrepancy model. The surrogate model is meant to be a statistical representation of the simulation results, circumventing issues arising from computational load, while the discrepancy is aimed to account for the difference between the simulation output and physical experiments. In this thesis, this Bayesian calibration framework is applied to a material bending problem, where in-situ mechanical characterization data and FEA simulations based on constitutive modeling are combined to produce updated values of the unknown material parameters with uncertainty.
  • Thumbnail Image
    Data-driven X-ray Tomographic Imaging and Applications to 4D Material Characterization
    Wu, Ziling (Virginia Tech, 2021-01-05)
    X-ray tomography is an imaging technique to inspect objects' internal structures with externally measured data by X-ray radiation non-destructively. However, there are concerns about X-ray radiation damage and tomographic acquisition speed in real-life applications. Strategies with insufficient measurements, such as measurements with insufficient dosage (low-dose) and measurements with insufficient projection angles (sparse-view), have been proposed to relieve these problems but are generally compromising imaging quality. Such a dilemma inspires the development of advanced tomographic imaging techniques, in particular, deep learning algorithms to improve reconstruction results with insufficient measurements. The overall aim of this thesis is to design efficient and robust data-driven algorithms with the help of prior knowledge from physics insights and measurement models. We first introduce a hierarchical synthesis CNN (HSCNN), which is a knowledge-incorporated data-driven tomographic reconstruction method for sparse-view and low-dose tomography with a split-and-synthesis approach. This proposed learning-based method informs the forward model biases based on data-driven learning but with reduced training data. The learning scheme is robust against sampling bias and aberrations introduced in the forward modeling. High-fidelity X-ray tomographic imaging reconstruction results are obtained with a very sparse number of projection angles for both numerical simulated and physics experiments. Comparison with both conventional non-learning-based algorithms and advanced learning-based approaches shows improved accuracy and reduced training data size. As a result of the split-and-synthesis strategy, the trained network could be transferable to new cases. We then present a deep learning-based enhancement method, HDrec (hybrid-dose reconstruction algorithm), for low-dose tomography reconstruction via a hybrid-dose acquisition strategy composed of textit{extremely sparse-view normal-dose measurements} and textit{full-view low-dose measurements}. The training is applied for each individual sample without the need of transferring the trained models for other samples. Evaluation of two experimental datasets under different hybrid-dose acquisition conditions shows significantly improved structural details and reduced noise levels compared to results with traditional analytical and regularization-based iterative reconstruction methods from uniform acquisitions under the same amount of total dosage. Our proposed approach is also more efficient in terms of single projection denoising and single image reconstruction. In addition, we provide a strategy to distribute dosage smartly with improved reconstruction quality. When the total dosage is limited, the strategy of combining a very few numbers of normal-dose projections and with not-too-low full-view low-dose measurements greatly outperforms the uniform distribution of the dosage throughout all projections. We finally apply the proposed data-driven X-ray tomographic imaging reconstruction techniques, HSCNN and HDrec, to the dynamic damage/defect characterization applications for the cellular materials and binder jetting additive manufacturing. These proposed algorithms improve data acquisition speeds to record internal dynamic structure changes. A quantitative comprehensive framework is proposed to study the dynamic internal behaviors of cellular structure, which contains four modules: (i) In-situ fast synchrotron X-ray tomography, which enables collection of 3D microstructure in a macroscopic volume; (ii) Automated 3D damage features detection to recognize damage behaviors in different scales; (iii) Quantitative 3D structural analysis of the cellular microstructure, by which key morphological descriptors of the structure are extracted and quantified; (iv) Automated multi-scale damage structure analysis, which provides a quantitative understanding of damage behaviors. In terms of binder jetting materials, we show a pathway toward the efficient acquisition of holistic defect information and robust morphological representation through the integration of (i) fast tomography algorithms, (ii) 3D morphological analysis, and (iii) machine learning-based big data analysis. The applications to two different 4D material characterization demonstrate the advantages of these proposed tomographic imaging techniques and provide quantitative insights into the global evolution of damage/defect beyond qualitative human observation.
  • Thumbnail Image
    Dynamic and Post-Dynamic Microstructure Evolution in Additive Friction Stir Deposition
    Griffiths, Robert Joseph (Virginia Tech, 2021-08-17)
    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.
  • Thumbnail Image
    Effects of Chemical and Structural Heterogeneity on the Tribocorrosion Resistance of Metals in Aqueous Solutions
    Wang, Wenbo (Virginia Tech, 2022-06-27)
    The corrosion-wear resistance tradeoff in conventional metals imposes a great challenge to their reliable long-term performance under extreme conditions where surface stress and corrosive environment coexist (i.e., tribocorrosion). In this work, strategies to introduce chemical and structural heterogeneity with controlled length-scale at nanometers were proposed and studied in three metallic systems (i.e., Zr-based, Al-based and Mg-based), in order to enhance their tribocorrosion resistance. In the first study, ZrCuNiAl thin film metallic glasses (TFMG) with either homogeneous or heterogeneous local composition were deposited by magnetron sputtering through controlling processing conditions (i.e., argon (Ar) pressure). It was found that the mechanical properties, wear, corrosion and tribocorrosion resistance of ZrCuNiAl TFMG were significantly affected by nanoscale chemical heterogeneity. As a result, nanoscale chemical heterogeneity promoted ductility but reduced hardness, which in turn weakened wear resistance. While, in the 0.6 M NaCl solution, the resistance to pitting corrosion and tribocorrosion was improved because the presence of nanoscale chemical heterogeneity facilitates to generate more protective passive layer with lower defect density and faster repassivated capability, compared to their homogenous counterparts. In the second study, nanoscale chemical and structural heterogeneity were introduced in Al by forming Al/X nanostructured metallic multilayers (NMMs), where X=Mg, Cu, and Ti. Compared to the respective monolithic films, the alternating nanolayer configuration not only increased strength due to the presence of abundant interfaces but also reduced surface activity and pitting susceptibility. The electrochemical performance was significantly affected by the interaction, i.e., galvanic effect, between Al layer and underlayer constituents, which in turn led to different tribocorrosion behaviors, Specifically, transmission electron microscopy revealed that the materials loss in Al/Mg and Al/Cu NMMs primarily resulted from corrosion, while Al/Ti was dominated by severe plastic deformation during tribocorrosion as a result of sustained surface passivity. Lastly, in the bulk biodegradable Mg alloys system, the surface was treated by femtosecond laser shock peening (fs-LSP) technique with ultra-low pulse energy to introduce structural heterogeneity. Treatment conditions (e.g., power density, direct ablation and confined ablation) significantly affected the ultimate peening effect and further surface performance. In this work, the optimized peening effect was obtained at 28 GW/cm2 laser power density in the confined ablation with the assistance of the adsorption layer and confining medium. Combined with transmission electron microscopy and finite element analysis, the improvement of surface performance was attributed to high dislocation density near the surface, rather than compressive residual stress. The existence of structural heterogeneity not only reduced corrosion kinetics but simultaneously improved the self-repassivation in the blood bank buffered saline solution at body temperature.
  • Thumbnail Image
    Effects of Hot Isostatic Pressing on Copper Parts Additively Manufactured via Binder Jetting
    Yegyan Kumar, Ashwath (Virginia Tech, 2018-04-13)
    Copper is a material of interest to Additive Manufacturing (AM) owing to its outstanding material properties, which finds use in enhanced heat transfer and electronics applications. Its high thermal conductivity and reflectivity cause challenges in the use of Powder Bed Fusion AM systems that involve supplying high-energy lasers or electron beams. This makes Binder Jetting a better alternative as it separates part creation (binding together of powders) from energy supply (post-process sintering). However, it is challenging to fabricate parts of high density using this method due to low packing density of powder while printing. This work aims to investigate the effects of Hot Isostatic Pressing (HIP) as a secondary post-processing step on the densification of Binder Jet copper parts. By understanding the effects of HIP, the author attempts to create parts of near-full density, and subsequently to quantify the effects of the developed process chain on the material properties of resultant copper parts. The goal is to be able to print parts of desired properties suited to particular applications through control of the processing conditions, and hence the porosity. First, 99.47% dense copper was fabricated using optimized powder configurations and process parameters. Further, the HIP of parts sintered to three densities using different powder configurations was shown to result in an improvement in strength and ductility with porosity in spite of grain coarsening. The strength, ductility, thermal and electrical conductivity were then compared to various physical and empirical models in the literature to develop an understanding of the process-property-performance relationship.
  • Thumbnail Image
  • Thumbnail Image
    Frequency Response Modeling of Additive Friction Stir Deposition Parts with Print Defects
    Pennington, Brett Kenneth (Virginia Tech, 2024-06-03)
    A change in a part's response to vibrations can be measured and utilized as a non-destructive testing method to detect deviations in the part's materials or geometry through processes such as laser acoustic resonance spectroscopy. This work focuses on leveraging vibration resonance to detect flaws in prints produced through additive friction stir deposition that arise through environmental contamination. More specifically, the use case considered is the printing of AA7075 in an iron oxide rich environment, where iron oxide dust or powder could accidentally be stirred into the printed material creating a print flaw. The modeling of printed parts contaminated with iron oxide to predict their natural frequencies is examined. Two different finite element models are discussed, which were created to represent contamination flaws with and without voids. The first model considers the case where a part is void-free. In this case, the model assumes a solid, homogeneous material condition in the stir region. The second model considers the case where voids are present in the part. This model leverages x-ray computed tomography data to build a representative mesh. These models show that with a well-understood part and corresponding flaw, it is possible to predict the natural frequencies of a flawed part. By leveraging the part vibration measurements and model predictions of known defects, it may be possible to gain insights into and characterize unknown print flaws.
  • Thumbnail Image
    Granular Shape Memory Ceramics
    Rauch, Hunter (Virginia Tech, 2021-05-05)
    Shape memory ceramics (SMCs) are burgeoning functional materials based on zirconia with a reversible, stress-inducible martensitic phase transformation. Compared to metallic shape memory alloys, SMCs have broader operating temperatures, higher critical stresses, and larger mechanical hysteresis loops. These advantages make SMCs attractive for high-output actuation and sensing in extreme environments or energy dissipation applications; however, the key phase transformation generates large stresses and strains that accumulate at grain boundaries and result in fracture of monolithic SMCs. This means that material forms with decreased mechanical constraint are necessary. Transformation without fracture has been previously demonstrated with SMC micropillars and individual microparticles, but these material forms lack useful applications. By utilizing easily scalable granular packings of discrete free particles, the transformation can be triggered in bulk without fracture in much the same way. The granular packing material form introduces significant complexity as the internal stress distributions responsible for the phase transformation are highly heterogeneous on the macro-, meso-, and micro-scales. Moreover, the unconstrained phase transformation behaves differently than the constrained transformation, which is more studied in zirconia. The interactions of these various factors are explored from a fundamental perspective in this work, notably including (1) a unique 'continuous mode' of both forward and reverse transformation in granular packings, (2) the dependence of transformation behavior on macro-, meso-, and microstructure, and (3) the evolution of the granular packings' structure and energy dissipation capacity over 10,000 loading cycles. Diverse experimental techniques are employed, ranging from mechanical testing and calorimetry to in situ neutron diffraction, to support theory based on the martensitic phase transformation in zirconia, the shape memory and superelastic effects, and granular material physics.
  • Thumbnail Image
    Heat Generation and Transfer in Additive Friction Stir Deposition
    Knight, Kendall Peyton (Virginia Tech, 2024-05-31)
    Additive friction stir deposition (AFSD) is an emerging solid-state additive manufacturing process that leverages the friction stir principle to deposit porosity-free material. The unique flow of material that allows for the transformation of bar stock into a near-net shape part is driven by the non-linear heat generation mechanisms of plastic deformation and sliding frictional heat generation. The magnitude of these mechanisms, and hence the total applied thermal power, implicitly depend on the thermal state of the system, forcing power input to become a dependent variable. This is not the case in other 3D printing methods; thermal power can be controlled independently. In this work, the heat generation in AFSD is explored, and its transfer is quantified. In particular, the time-dependent ratio between the amount of conduction into the AFSD tool versus into the substrate is quantified. It was found for the conditions tested with a single-piece AFSD tool, conduction up the tool was on the order of the conduction into the stir. For a more modern three-piece tool, the ratio between the tool and the substrate varied between 0.3-0.1. It was also found that traversing faster resulted in more heat flux into the substrate as would be expected by moving heat source modeling. The total heat generated was also quantified as being equal to between 60% and 80% of the mechanical spindle power depending on the tool type and the exact process conditions. That ratio was found to be time-invariant. At the same time, this changing heat flux ratio was shown to dramatically alter thermocouple measurements in the tool, showing the uncertainty of that method of process control. The contact state between the stir and the tool was treated as a thin conductive layer and a contact heat transfer coefficient was calculated on the order of 20 frac{kW}{m^2K}. The limitations of this treatment were found to occur when a significant amount of the heat generation came from frictional heating rather than plastic deformation. This implies that any measurement conducted in the tool is related to the stir by a complex function driven by the state of the stir; showcasing the need for more well-understood in-situ monitoring. Finally, some of the ideas about thermal control are applied to a case study on the repair of corroded through holes using AFSD to restore fatigue life. It was found that modifying the thermal boundary conditions and applying active cooling at the end of the repair could improve the fatigue life drastically. This was due to less time spent in a thermally active region leading to less heterogeneous nucleation and less grain boundary nucleation. This more preferred microstructure morphology led to a change in the fracture mode and increased the number of cycles to crack initiation and the number of cycles after crack initiation.
  • Thumbnail Image
    Integration of Physically-based and Data-driven Approaches for Thermal Field Prediction in Additive Manufacturing
    Li, Jingran (Virginia Tech, 2017)
    A quantitative understanding of thermal field evolution is vital for quality control in additive manufacturing (AM). Because of the unknown material parameters, high computational costs, and imperfect understanding of the underlying science, physically-based approaches alone are insufficient for component-scale thermal field prediction. Here, I present a new framework that integrates physically-based and data-driven approaches with quasi in situ thermal imaging to address this problem. The framework consists of (i) thermal modeling using 3D finite element analysis (FEA), (ii) surrogate modeling using functional Gaussian process, and (iii) Bayesian calibration using the thermal imaging data. Based on heat transfer laws, I first investigate the transient thermal behavior during AM using 3D FEA. A functional Gaussian process-based surrogate model is then constructed to reduce the computational costs from the high-fidelity, physically-based model. I finally employ a Bayesian calibration method, which incorporates the surrogate model and thermal measurements, to enable layer-to-layer thermal field prediction across the whole component. A case study on fused deposition modeling is conducted for components with 7 to 16 layers. The cross-validation results show that the proposed framework allows for accurate and fast thermal field prediction for components with different process settings and geometric designs.
  • Thumbnail Image
    Investigation of the Processing History during Additive Friction Stir Deposition using In-process Monitoring Techniques
    Garcia, David (Virginia Tech, 2021-02-01)
    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.
  • Thumbnail Image
    Mask Projection Microstereolithography 3D Printing of Gelatin Methacrylate
    Surbey, Wyatt R. (Virginia Tech, 2019-06-18)
    Gelatin methacrylate (GelMA) is a ubiquitous biocompatible photopolymer used in tissue engineering and regenerative medicine due to its cost-effective synthesis, tunable mechanical properties, and cellular response. Biotechnology applications utilizing GelMA have ranged from developing cell-laden hydrogel networks to cell encapsulation and additive manufacturing (3D printing). However, extrusion based 3D printing is the most common technique used with GelMA. Mask projection microstereolithography (MPµSL or µSL) is an advanced 3D printing technique that can produce geometries with high resolution, high complexity, and feature sizes unlike extrusion based printing. There are few biomaterials available for µSL applications, so 3D printing GelMA using µSL would not only add to the repertoire materials, but also demonstrate the advantages of µSL over other 3D printing techniques. A novel GelMA resin was tested with µSL to create a porous scaffold with a height and print time that has not been displayed in the literature before for a scaffold of this size. The resin consists of GelMA, deionized water, lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP, photoinitiator), and 2-Hydroxy-4-methoxybenzophenone-5-sulfonic acid (sulisobenzone, UV blocker) and can be processed at room temperature. Four resins were tested (w/w %) and characterized for µSL printing: 20% GelMA 0.5% UV blocker, 20% GelMA 1.0% UV blocker, 30% GelMA 0.5% UV Blocker, and 30% GelMA 1.0% UV blocker. Swell testing, working curve, photo-rheology, photo-DSC (dynamic scanning calorimetry), 3D printing, and cell culture tests were performed and results showed that 30% GelMA 1.0% UV blocker had the best 3D print fidelity among resin compositions.
  • Thumbnail Image
    Material Flow and Microstructure Evolution during Additive Friction Stir Deposition of Aluminum Alloys
    Perry, Mackenzie Elizabeth Jones (Virginia Tech, 2021-09-02)
    Serious issues including solidification porosity, columnar grains, and large grain sizes are common during fusion-based metal additive manufacturing due to the inherent melting and solidification that occurs during printing. In recent years, a high-temperature, rapid plastic deformation technique called additive friction stir deposition (AFSD) has shown great promise in overcoming these issues. Because the deposited material stays in the solid state during printing, there are no melting and solidification events and the process can result in as-printed material that is fully-dense with equiaxed, fine grains. As AFSD is an emerging process, developing an understanding of the synergy between material deformation and the resultant microstructure evolution, especially the strain magnitude, its influence on dynamic microstructure evolution, and material flow details, is imperative for the full implementation of AFSD. Therefore, the purpose of this work is to investigate the severe plastic deformation in AFSD through complementary studies on the concurrent evolution of shape and microstructure during the deposition of dissimilar aluminum alloys. In this work, we systematically study (1) the entire deposition via dissimilar cladding along with (2) specific volumes within the deposited layer via embedded tracers printed at varied processing parameters. X-ray computed tomography and electron backscatter diffraction are employed to visualize the complex shape of the deposits and understand the microstructure progression. Investigation of dissimilar cladding of homogeneous AA2024 feed-rods onto an AA6061 substrate establishes a working understanding of the mechanisms related to material flow and microstructure evolution across the whole deposit (macroscopic shape evolution) as well as at the interface between the deposit and the substrate. Variations in tooling and rotation rate affect the interfacial features, average grain size, and depth of microstructural influence. The non-planar and asymmetric nature of AFSD on the macro-scale is revealed and a maximum boundary of deposited material is established which gives a frame of reference for the next material flow study within the deposition zone. An understanding of the mesoscopic morphological evolution and concurrent dynamic microstructure evolution of representative volumes within the deposition zone is determined by comparing depositions of hybrid feed-rods (AA6061 matrix containing an embedded tracer of AA2024). Samples were printed with and without an in-plane velocity to compare initial material feeding to steady-state deposition. Variations in initial tracer location and tool rotation rate/in-plane velocity pairs affect the final morphology, intensity of mixing, and microstructure of the deposited tracer material. The tracer material undergoes drastic mesoscopic shape evolution from millimeter-scale cylinders to long, curved micro-ribbons. There is simultaneous grain refinement in AA2024 via geometric dynamic recrystallization during initial material feeding, after which the grain size remains relatively constant at a steady-state size. The lower bound of strain is estimated based on extrusion, torsion, and shear-thinning factors. The step-by-step mesoscopic deformation and microstructure evolution is further elucidated by characterizing depositions of hybrid feed-rods with a series of embedded tracers. The AFSD tooling is stopped quickly at the end of the deposition with a quench applied to "freeze" the sample. X-ray computed tomography reveals multiple intermediate morphologies including the progression from a cylinder to a tight spiral, to a flattened spiral shape, and to a thin disc. EBSD mapping shows that a refined microstructure is formed soon after the material leaves to tool head with areas off the centerline reaching a fully recrystallized state more quickly. The findings from this work summarize the current understanding of the link between material deformation and microstructure evolution in AFSD. Hopefully these first fundamental studies on the co-evolution of material flow and grain structure during AFSD can inspire future work, especially in the area of heterogeneous multi-material printing.
  • Thumbnail Image
    Mechanical and Physical Properties in Additive Friction Stir Deposited Aluminum
    Wells, Merris Corinne (Virginia Tech, 2022-07-18)
    The goal of this research is to aid the development of large-scale additive manufacturing of jointless underbody hulls for the Army Ground Vehicle Systems by 1) generating an improved mechanical and metallurgical database and 2) understanding the Additive Friction Stir Deposition (AFSD) process. AFSD is a solid-state additive manufacturing process that is a high strain rate and a hot working process that deforms material onto a substrate and builds a component layer by layer. This unique, solid-state additive manufacturing process has the potential for scalability into ground vehicle applications on the extra large-scale due to its solid-state nature. Two different aluminum alloys were investigated: Al-Mg-Si (6061) and Al-Zn-Mg-Cu (7075). AFSD builds were evaluated in the transverse or through layer direction (Z) and the 6061 material was also evaluated in the longitudinal direction (X). Uniaxial tensile testing was performed to generate mechanical property data while fractography, and metallography were used to better understand the metallurgical implications of this process. This research determined that the refinement of the grain size caused by the AFSD process had little or no strengthening effect on the mechanical properties of either alloy. Instead, the as-deposited condition in both alloys were soft with good ductility due to the dissolution of the strengthening particles. After heat treatment, the elongation and fracture mode of the 6061 alloy was dependent on the layer direction. Failure often initiated at interfaces and affected the materials' elastic-plastic behavior. For the 7075 alloy, the strength and failure mechanism of the material were affected by the presence of the graphite lubricant used during processing. The use of graphite increased the variability of the mechanical properties results and caused premature failure in numerous samples. In both alloys, the heat treatment caused grain coarsening to varying degrees which can affect the mechanical behavior. From these results, it was found that a precipitation strengthening heat treatment is required for material deposited with AFSD to achieve the minimum mechanical property standards for a forging. Recommendations and future work include 1) investigating the effect of residual stresses on AFSD components, 2) determining the fatigue properties of AFSD materials, 3) continuing to increase the database of mechanical properties for AFSD materials, and 4) developing additional lubricants for the AFSD process.
  • Thumbnail Image
    Microstructure Representation and Prediction via Convolutional Neural Network-Based Texture Representation and Synthesis, Towards Process Structure Linkage
    Han, Yi (Virginia Tech, 2021-05-19)
    Metal additive manufacturing (AM) provides a platform for microstructure optimization via process control, the ability to model the evolution of microstructures from changes in processing condition or even predict the microstructures from given processing condition would greatly reduce the time frame and the cost of the optimization process. In 1, we present a deep learning framework to quantitatively analyze the microstructural variations of metals fabricated by AM under different processing conditions. We also demonstrate the capability of predicting new microstructures from the representation with deep learning and we can explore the physical insights of the implicitly expressed microstructure representations. We validate our framework using samples fabricated by a solid-state AM technology, additive friction stir deposition, which typically results in equiaxed microstructures. In 2, we further improve and generalize the generating framework, a set of metrics is used to quantitatively analyze the effectiveness of the generation by comparing the microstructure characteristics between the generated samples and the originals. We also take advantage of image processing techniques to aid the calculation of metrics that require grain segmentation.
  • Thumbnail Image
    Multi-Physics Sensing and Real-time Quality Control in Metal Additive Manufacturing
    Wang, Rongxuan (Virginia Tech, 2023-06-08)
    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.