Doctor of Philosophy, The Ohio State University, 2022, Welding Engineering

Ductility-dip cracking (DDC) in face-centered cubic (FCC) alloys, such as nickel-based alloys and 300-series stainless steels, is a challenge faced by nuclear power generation. Aging reactors need to be repaired via multipass weld overlays to extend their lifetime. DDC often occurs in the first few layers of these overlays, and the nuclear industry has low flaw tolerance, making DDC subject to costly repair and rework. The prevailing theory describing DDC is based on observations of grain boundary (GB) sliding, microvoid formation, and the effect of GB tortuosity. This work aims to quantify the effect welding process parameters and welding generated stresses have on the formation of DDC and to provide clear avenues for productive future research. The main project objectives include the development of methodology, based on combining physical experiments and computational modeling, for prediction of DDC in multipass welds of austenitic alloys that is applicable for materials selection and process optimization. An additional study on the DDC fracture surface was conducted due to findings from the experimental component. Research began with the development of a Gleeble-based experimental procedure that evaluates a material's susceptibility to elevated temperature embrittlement. The procedure is called simulated strain ratcheting (SSR), and preliminary testing led to the use of the imposed mechanical energy (IME), defined as the integral of experienced stress vs. strain, as a parameter for quantification of thermo-mechanical loading in Gleeble tests and FEA models of multipass welds. This experimental procedure was used to successfully generate DDC in various nickel-based alloys and 310 stainless steel. Fracture surfaces generated from this testing were found to exhibit thermal faceting (TF), which warranted further study. Samples which contained high amounts of DDC, or those which experienced fracture, also generally experience higher IME than those which showed no s (open full item for complete abstract)

Committee: Boian Alexandrov (Advisor); Avraham Benatar (Committee Member); Carolin Fink (Committee Member); John Lippold (Committee Member); Michael Mills (Committee Member) Subjects: Engineering; Materials Science; Metallurgy

Doctor of Philosophy, The Ohio State University, 2020, Materials Science and Engineering

Traditionally, the design and eventual casting of engineering components has been plagued by the assumption of homogeneous mechanical properties across the whole of the casting. Such an assumption is rarely the case and often leads to overdesign and excessive downstream waste. To combat this, the Integrated Computation Materials Engineering (ICME) approach was implemented to provide an increased level of accuracy for the prediction of location specific properties in cast aluminum. Variable properties often result from variable cooling rates that occur in a structure resulting from cooling lines or chill blocks located within the mold package, variable alloying content and finally the defects present in the alloy. In the context of this research, variable cooling was studied using the concept of a maximum chill scenario where the heat transfer coefficient (HTC) was examined and found to develop over time. The resulting microstructure was included an extremely refined eutectic silicon within the interdendritic regions. Unique, time-dependent HTC maps were created for optimized maximum chill scenario casting conditions to provide unique cooling and solidification conditions across the whole of a casting. The effect of Magnesium on hypoeutectic Al-Si alloys Primary Dendrite Arm Spacing (PDAS) was examined next. Directional solidification experimentation was coupled with Cellular Automaton (CA) simulations to develop a model to predict the location specific PDAS within Al-Si-Mg alloys. Additionally, location specific properties are known to result from defects within the microstructure such oxide inclusions and porosity. These voided regions act as regions of increased stress and result in premature failure of engineered components. In this work the fluid flow, filling conditions and free surface of the molten aluminum was examined to create a new model that was accurately shown to predict the location specific defect content in a cast structure. Finally, the sum of thi (open full item for complete abstract)

Committee: Alan Luo (Advisor); Glenn Daehn (Committee Member); Stephen Niezgoda (Committee Member) Subjects: Engineering; Materials Science

Doctor of Philosophy, The Ohio State University, 2019, Materials Science and Engineering

Metal casting is a manufacturing process of solidifying molten metal in a mold to make a product with a desired shape. Based on its own unique fabrication benefits, it is one of the most widely used manufacturing processes to economically produce parts with complex geometries in modern industry, especially for transportation and heavy equipment industries where mass production is needed. However, various types of defects typically exist in the as-cast components during the casting processes, which may make it difficult for post-processing and limit the service life and further application of products. It becomes imperative to analyze the processes in actual manufacturing conditions to predict and prevent those casting defects. Since it can be quite time consuming and costly to assess the processes experimentally, a computer-aided approach is highly desirable for product development and process optimization. In recent decades, computer-aided engineering (CAE) techniques have been rapidly developed to simulate different casting processes, which have great benefits to tackle casting defects in a more practical and efficient way. This work focuses on using ProCAST®, a finite element analysis (FEA) software, together with other necessary simulation and modeling techniques, including Computer-Aided Design (CAD), Calculation of Phase Diagrams (CALPHAD) and Cellular Automaton (CA), to study relevant defects in actual metal casting foundries. Specifically, three different cases have been mainly investigated, including (i) veining defect caused by thermal cracking in resin-bonded silica sand molds/inserts for sand casting process; (ii) thermal fatigue cracking in H13 steel dies/inserts for high pressure die casting process; and (iii) Hydrogen-induced gas porosity in A356 castings for gravity casting process with permanent molds. For each case, CAD model was designed and FEA model was constructed with validated materials database based on CALPHAD simulation, experimen (open full item for complete abstract)

Committee: Alan Luo (Advisor); Glenn Daehn (Committee Member); Wei Zhang (Committee Member) Subjects: Engineering; Materials Science

Doctor of Philosophy, The Ohio State University, 2019, Materials Science and Engineering

The design framework for complex materials property and processing models within the Integrated Computational Material Engineering (ICME) is often hindered by the expensive computational cost. The ultimate goal of ICME is to develop data-driven, materials-based tools for the concurrent optimization of material systems while, improving the deployment of innovative materials in real-world products. Reduced-order, fast-acting tools are essential for both bottom-up property prediction and top-down model calibrations employed for modern material design applications. Additionally, reduced-order modeling requires formal uncertainty quantification (UQ) from the processing stages all the way down to the manufacturing and component design. The goal of this thesis is to introduce a machine learning-based, reduced-order crystal plasticity model for face-centered cubic (FCC) polycrystalline materials. This implementation was founded upon Open Citrination, an open-sourced materials informatics platform. Case studies for both the bottom-up property prediction and top-down optimization of model parameters are demonstrated within this work. The proposed reduced-order model is used to correctly approximate the plastic stress-strain curves and the texture evolution under a range of deformation conditions and strain rates specific to a material. The inverted pathway is applied to quickly calibrate the optimal crystal plasticity hardening parameters given the macroscale stress-strain responses and evolutionary texture under certain processing conditions. A visco-plastic self-consistent (VPSC) method is used to create the training and validation datasets. The description of the material texture is given through a dimension reduction technique which was implemented by principal component analysis (PCA). The microstructures of engineering materials typically involve an intricate hierarchical crystallography, morphology, and composition. Therefore, an accurate, virtual representation (open full item for complete abstract)

Committee: Stephen Niezgoda (Advisor); Yunzhi Wang (Committee Member); Michael Groeber (Committee Member); Maryam Ghazisaeidi (Committee Member) Subjects: Materials Science