Parts made out of titanium alloys demonstrate anisotropic mechanical properties when manufactured by electron beam melting, an emerging additive manufacturing technique. Understanding the process history dependent heterogeneous microstructure, and its effect on mechanical properties is crucial in determining the performance of additively manufactured titanium alloys as the mechanical behavior heavily relies on the underlying microstructural features. This thesis work focuses on combined experimental and computational techniques for microstructure characterization, synthetic microstructure generation, mechanical property measurement, and mechanical behavior modeling of polycrystalline materials, with special focus on dual phase titanium alloys. Macroscopic mechanical property measurements and multi-modal microstructure characterizations (high energy X-ray diffraction, computed tomography and optical microscopy) are performed on additively manufactured Ti-6Al-4V parts, revealing the heterogeneity of the microstructure and properties with respect to the build height. Because characterizing and testing every location within a build is not practical, a computational methodology is established in order to reduce the time and cost spent on microstructure-property database creation. First a statistical volume element size is determined for the Fast Fourier Transform based micromechanical modeling technique through a sensitivity study performed on an experimental Ni-based superalloy and syntheticW, Cu, Ni and Ti structures, showing that as the contrast of properties (e.g., texture, field localization, anisotropy, rate-sensitivity) increases, so does the minimum simulation domain size requirement. In all deformation regimes a minimum volume element is defined for both single and dual phase materials. The database is then expanded by generating statistically representative Ti structures which are modified for features of interest, e.g., lath thickness, grain size and orientation distribution, to be used in spectral full-field micromechanical modeling. The relative effect of the chosen microstructural features is quantified through comparisons of average and local field distributions. Fast Fourier transform based technique, being a spectral, full-field deformation modeling tool, is shown to be capable of capturing the relative contribution from varying microstructural features such as phase fractions, grain morphology/ size and texture on the overall mechanical properties as the results indicate that the mean field behavior is predominantly controlled by the alpha phase fraction and the prior beta phase orientation.

<p dir="ltr">In structural metallic components, a material’s lifecycle begins with the processing route, to produce a desired structure, which dictates the in-service performance. The variability of microstructural features as a consequence of the processing route has a direct influence on the properties and performance of a material. In order to correlate the influence processing conditions have on material performance, large test matrices are required which tend to be time consuming and expensive. An alternative route to avoid such large test matrices is to incorporate physics-based process modeling and lifing paradigms to better understand the performance of structural materials. By linking microstructural information to the material’s lifecycle, the processing path can be modified without the need to repeat large-scale testing requirements. Additionally, when a materials system is accurately modeled throughout its lifecycle, the performance predictions can be leveraged to improve the design of materials and components.</p><p dir="ltr">Ni-based superalloys are a material class widely used in many critical aerospace components exposed to coupling thermal and mechanical loads due to their increased resistance to creep, corrosion, oxidation, and strength characteristics at elevated temperatures. Many Ni-based superalloys undergo high-temperature forging to produce a desired microstructure, targeting specific strength and fatigue properties in order to perform under thermo-mechanical loads. When in-service, these alloys tend to fail as a consequence of thermo-mechanical fatigue (TMF) from either inclusion- or matrix- driven failure. In order to produce safer, cheaper and more efficient critical aerospace components, the micromechanical deformation and damage mechanisms throughout a Ni-based superalloy’s lifecycle must be understood. This research utilizes process modeling as a tool to understand the damage and deformation of inclusions in a Ni-200 matrix throughout radial forging as a means to optimize the processing conditions for improved fatigue performance. In addition, microstructural sensitive performance modeling for a Ni-based superalloy is leveraged to understand the influence TMF has on damage mechanisms.</p><p dir="ltr">The radial forging processing route requires both high temperatures and large plastic deformation. During this process, non-metallic inclusions (NMIs) can debond from the metallic matrix and break apart, resulting in a linear array of smaller inclusions, known as stringers. The evolution of NMIs into stringers can result in matrix load shedding, localized plasticity, and stress concentrations near the matrix-NMI interface. Due to these factors, stringers can be detrimental to the fatigue life of the final forged component. By performing a finite element model of the forging process with cohesive zones to simulate material debonding, this research contributes to the understanding of processing induced deformation and damage sequences on the onset of stringer formation for Alumina NMIs in a Ni-200 matrix. Through a parametric study, the interactions of forging temperature, strain rate, strain per pass, and interfacial decohesion on the NMI damage evolution metrics are studied, specifically NMI particle separation, rotation, and cavity formation. The parametric study provides a linkage between the various processing conditions parameters influence on detrimental NMI morphology related to material performance.</p><p dir="ltr">The microstructural characteristics of Ni-based superalloys, as a consequence of a particular processing route, creates a variability in TMF performance. The micromechanical failure mechanisms associated with TMF are dependent on various loading parameters, such as temperature, strain range, and strain-temperature phasing. Insights on the complexities of micromechanical TMF damage are studied via a temperature-dependent, dislocation density-based crystal plasticity finite element (CPFE) model with uncertainty quantification. The capabilities of the model’s temperature dependency are examined via direct instantiation and comparison to a high-energy X-ray diffraction microscopy (HEDM) experiment under coupled thermal and mechanical loads. Unique loading states throughout the experiment are investigated with both CPFE predictions and HEDM results to study early indicators of TMF damage mechanisms at the grain scale. The mesoscale validation of the CPFE model to HEDM experimental data provides capabilities for a well-informed TMF performance paradigm under various strain-temperature phase profiles. </p><p dir="ltr">A material’s TMF performance is highly dependent on the temperature-load phase profile as a consequence of path-dependent thermo-mechanical plasticity. To investigate the relationship between microstructural damage and TMF phasing effects, the aforementioned CPFE model investigates in-phase (IP) TMF, out-of-phase (OP) TMF, and iso-thermal (ISO) loading profiles. A microstructural sensitive performance modeling framework with capabilities to isolate phasing (IP, OP, and ISO) effects is presented to locate fatigue damage in a set of statistically equivalent microstructures (SEMs). Location specific plasticity, and grain interactions are studied under the various phasing profiles providing a connection between microstructural material damage and TMF performance.</p>

Additive manufacturing (AM) introduces high variability in the microstructure and defect distributions, compared with conventional processing techniques, which introduces greater uncertainty in the resulting fatigue performance of manufactured parts. As a result, qualification of AM parts poses as a problem in continued adoption of these materials in safety-critical components for the aerospace industry. Hence, there is a need to develop precise and accurate, physics-based predictive models to quantify the fatigue performance, as a means to accelerate the qualification of AM parts. The fatigue performance is a critical requirement in the safe-life design philosophy used in the aerospace industry. Fatigue failure is governed by the loading conditions and the attributes of the material microstructure, namely, grain size distribution, texture, and defects. In this work, the crystal plasticity finite element (CPFE) method is employed to model the microstructure-based material response of an additively manufactured Ni-based superalloy, Inconel 718 (IN718). Using CPFE and associated experiments, methodologies were developed to assess multiple aspects of the fatigue behavior of IN718 using four studies. In the first study, a CPFE framework is developed to estimate the critical characteristics of porosity, namely the pore size and proximity that would cause a significant debit in the fatigue life. The second study is performed to evaluate multiple metrics based on plastic strain and local stress in their ability to predict both the modes of failure as seen in fractography experiments and estimate the scatter in fatigue life due to microstructural variability as obtained from fatigue testing. In the third study, a systematic analysis was performed to investigate the role of the simulation volume and the microstructural constraints on the fatigue life predictions to provide informed guidelines for simulation volume selection that is both computationally tractable and results in consistent scatter predictions. In the fourth study, validation of the CPFE results with the experiments were performed to build confidence in the model predictions. To this end, 3D realistic microstructures representative of the test specimen were created based on the multi-modal experimental data obtained from high-energy diffraction experiments and electron backscatter diffraction microscopy. Following this, the location of failure is predicted using the model, which resulted in an unambiguous one to one correlation with the experiment. In summary, the development of microstructure-sensitive predictive methods for fatigue assessment presents a tangible step towards the adoption of model-based approaches that can be used to compliment and reduce the overall number of physical tests necessary to qualify a material for use in application.