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Ultra-fine grained materials with sub-micrometer grain size exhibit superior mechanical properties when compared with conventional fine-grained material as well as coarse-grained materials. Severe plastic deformation (SPD) techniques have been shown to be an effective way to modify the microstructure in order to improve the mechanical properties of the material. Crystalline materials require dislocations to accommodate plastic strain gradients and maintain lattice continuity. The lattice curvature exists due to the net dislocation that left behind in material during deformation. The characterization of such defects is important to understand deformation accumulation and the resulting mechanical properties of such materials. However, traditional techniques are limited. For example, the spatial resolution of EBSD is insufficient to study materials processed via SPD, while high dislocation densities make interpretations difficult using conventional diffraction contrast techniques in the TEM. A new technique, precession electron diffraction (PED) has gained recognition in the TEM community to solve the local crystallography, including both phase and orientation, of nanocrystalline structures under quasi-kinematical conditions. With the assistant of precession electron diffraction coupled ASTARÔ, the structure evolution of equal channel angular pressing processed commercial pure titanium is studied; this technique is also extended to two-phase titanium alloy (Ti-5553) to investigate the existence of anisotropic deformation behavior of the constituent alpha and beta phases.

Further understanding of dislocation-GB interactions is critical to increasing the performance of polycrystalline metals. The research contained within this dissertation aims to further dislocation-GB interaction understanding through three research studies. First, the effect of noise in EBSPs on GND calculations was evaluated in order to improve dislocation characterization via HR-EBSD. Second, the evolution of GNDs and their effects on back stress was studied through experimental and computational methods applied to tantalum oligo specimens. Third, statistical analysis was used to evaluate grain parameters and current GB transmission parameters on their correlation with dislocation accumulation.

Multiple strain path changes during forming lead to complex geometrically necessary dislocation (GND) development in strain gradient fields, inducing internal stresses that contribute to the Bauschinger effect, residual stresses, and springback which alters the final geometrical shape of the part. In order to analyze and design improved processing routes, models must capture the evolution of these internal stresses. However, most models capture the effects of these stresses via phenomenological approaches that require calibration to each new material and strain path. The development of models that capture the underlying physics at the sub-grain level is underway but requires in-depth studies of dislocation behavior (at the relevant meso length scale) in order to guide and validate them. The novel experimental campaign central to this thesis aims to tackle this problem by capturing unprecedented data of dislocation activity for several sheet metals during multiple strain path deformation. The resultant insights provide a new window into multi-path forming of metals, while also aiding the development and validation of two crystal plasticity (CP) models by collaborators at the University of New Hampshire (UNH). The models incorporate internal stresses at the grain and sub-grain levels, respectively. The hardening response due to strain path change during forming of AA6016-T4 was studied at the macro- and micro-level via combined experiments and an elasto-plastic self-consistent (EPSC) model. The experiments demonstrated that possible recombination and/or redirection of dislocations onto different slip systems under strain path change allowed for a gradual elasto-plastic transition, in comparison to a much sharper response upon continued deformation under the same strain path due to buildup and immediate activation of backstresses. The phenomenological backstress law of the EPSC model underpredicted the yield stress response for the strain path change deformations, possibly due to missing sub-grain GND development and an accurate description of associated backstresses. A more detailed experimental study of multi-path deformation for the AA6016-T4 was required in order to guide development of a strain gradient elasto-visco plasticity self-consistent model (SG-EVPSC); the model includes sub-grain strain gradient fields, and related internal stress fields. Total dislocation and GND density were tracked at various points of the deformation, and a complete 3D statistical volume element was characterized, to enable accurate modeling of the microstructure. The tests revealed a relatively lower yield stress response following strain path change, presumably aided by lower latent hardening than self hardening; the tests then showed a rapid accumulation of dislocations on the newly activated slip systems resulting in much higher final dislocation density without affecting the ductility of the pre-strained material. Interestingly, GND development was dominated by the precipitates instead of grain boundaries. These observations are vital for an accurate forming prediction from CPFEA models. Finally, optimized forming conditions of continuous bending under tension produced a ratcheting strain path resulting in a gradual GND development and a more complete retained austenite transformation in quenched-&-partitioned- and TRIP-assisted bainitic ferritic-1180 steels increasing their ductility by at least 360%.

1. GND 2. RDA 3. CrissCross 4. MACS 5. Petrus

1. GND 2. RDA 3. CrissCross 4. MACS 5. Petrus

An artificial neural network (ANN) is used to model nonlinear, large deformation plastic behavior of a material. This ANN model establishes a relationship between flow stress and dislocation structure content. The density of geometrically necessary dislocations (GNDs) was calculated based on analysis of local lattice curvature evolution. The model includes essential statistical measures extracted from the distributions of dislocation microstructures, including substructure cell size, wall thickness, and GND density as the input variables to the ANN model. The model was able to successfully predict the flow stress of aluminum alloy 6022 as a function of its dislocation structure content. Furthermore, a sensitivity analysis was performed to identify the significance of individual dislocation parameters on the flow stress. The results show that an ANN model can be used to calibrate and predict inelastic material properties that are often cumbersome to model with rigorous dislocation-based plasticity models.