Development of reduced-order models for predicting the plastic deformation of metals employing material knowledge systems.

Metal alloys being explored for structural applications exhibit a complex polycrystalline internal structure that intrinsically spans multiple length-scales. Therefore, rational design efforts for such alloys require a multiscale modeling framework capable of adequately incorporating the appropriate physics that control/drive the plastic deformation at the different length scales when modeling the overall plastic response of the alloy. The establishment of the desired multiscale modeling frameworks requires the development of low-computational cost, non-iterative, frameworks capable of accurately localizing the anisotropic plastic response of polycrystalline microstructures. This dissertation addresses the outlined needs by defining suitable extensions to the scale-bridging, data-driven Material Knowledge System Framework. The extensions detailed in the subsequent chapters enabled the first successful implementation of this framework for predicting the plastic response of polycrystalline microstructures caused by any arbitrary periodic boundary condition imposed at the macroscale. The case studies presented in this work demonstrate that the localization models developed using the MKS framework are of low-computational cost and non-iterative. Nevertheless, their predictions are not as accurate as desired. As a result, leveraging the insights obtained from the implementation of this framework to polycrystalline plasticity, this dissertation provides a robust protocol to incorporate deep learning approaches in order to provide better predictions of the local plastic response in polycrystalline RVEs. The final case study performed in this dissertation establishes that the most robust approaches to develop accurate localization reduced-order models capable of accurately predicting the local anisotropic plastic response of polycrystalline microstructures are deep learning approaches such as Convolutional Neural Networks.Ph.D

Experimental investigation of microstructure and properties in structural alloys through image analyses and multiresolution indentation

This work addresses the challenges in the investigation of structural alloy microstructures and their mechanical properties at multiple length scales. The investigations are performed on small volume ferrite-pearlite steel samples that were excised from in-service gas turbine components after prolonged exposure (up to 99,000 hours) to elevated temperatures, which promotes microstructural changes (spheroidization of pearlite and graphitization) as well as their yield strengths. Recent advances in spherical indentation protocols are combined for the first time to investigate the mechanical response of microscale ferrite-pearlite constituents and estimates of bulk properties on macroscale. It is shown that indentation yield strength captured with large indenter tips on an ensemble of ferrite-pearlite grains correlate strongly to the bulk yield strength evaluated with tensile measurements. Measurements on the individual ferrite and pearlite constituents follow a similar trend of decreasing yield strength as the bulk measurements. Second, to advance the reliability and accuracy of microstructure characterization, an image segmentation framework is developed that consists of five main steps designed to achieve systematic image segmentation on broad classes of microstructures utilizing widely available image processing tools. The flexibility and modularity of the framework was demonstrated on various types of microstructures images. The developed framework was used to segment the microstructures of ferrite-pearlite samples. The extracted microstructure statistics from the segmented images and multiresolution indentation yield strength measurements were used to evaluate established composite theory estimates and have demonstrated highly consistent estimates for these material systems.Ph.D