UC Santa Barbara

Polycrystalline materials are characterized by complex microstructures, which present significant challenges in accurately predicting their overall properties. Among the critical factors complicating the prediction of mechanical properties is the heterogeneous nature of plastic deformation, manifested as localized slip bands in these materials. Microscopic slip within slip bands has been shown to govern macro-scale material properties, such as strength and toughness. Yet, our understanding of the spatial heterogeneity associated with slip remains surprisingly limited.

While ductility in polycrystalline materials stems from slip accumulation, localized slip is also implicated in crack initiation, propagation, and ultimate material failure. Recent experimental investigations have revealed that the observed trade-off between strength and toughness is attributed to the localized slip occurring within slip bands at the microscopic level. Over the past few decades, significant advancements in microscopy and characterization techniques have enabled researchers to capture slip bands and associated microstructural phenomena in polycrystals. However, these techniques are often expensive, challenging to interpret, and time-consuming to explore the multifaceted localization behavior under different conditions. Despite the breakthroughs in experimental capabilities, our computational capacity to model the development and implications of slip bands in polycrystalline solids have remained limited.

Motivated by recent advancements in slip band characterization and the growing demand for a numerical method to complement the experiments, this dissertation aims to develop a crystal plasticity-based computational framework to explicitly model slip bands and heterogeneous micromechanical fields associated with them. Slip bands are treated as discrete microstructural entities, enabling the computation of associated stress and strain fields. The proposed model is validated against a wide range of experimental results and subsequently applied to multiple microstructure settings to elucidate micro- and macro-scale phenomena observed in various polycrystalline materials. These phenomena include neighbor-affected slip band localization, the role of slip bands in triggering subsequent slip or twin bands or orientation deviation zones in the neighboring grain, and the link between macroscopic strength and microscopic slip in polycrystalline materials, to name a few. Explicit slip band modeling is, furthermore, combined with machine learning and data-driven approaches to design materials with superior, targeted properties, such as reduced anisotropy or strain delocalization in titanium alloys.

Integration of the framework developed here with novel experimental techniques holds immense potential to significantly advance our capabilities in predicting material properties. This transformative combination will bridge existing gaps in our understanding and enable more efficient and effective materials development, ultimately leading to improved material performance.