The aim of this thesis is to investigate the effects of additives, reactive elements and impurities, on the lifetime of thermal barrier coatings. The thesis consists of a number of studies on interface adhesion, impurity diffusion, grain boundary sliding and cleavage processes and their impact on the mechanical behaviour of grain boundaries. The effects of additives and impurity on interface adhesion were elaborated by using total energy calculations, electron localization and density of states, and by looking into the atomic separations. The results of these calculations allow the assessment of atomic level contributions to changes in the adhesive trend. Formation of new bonds across the interface is determined to improve the adhesion in reactive element(RE)-doped structures. Breaking of the cross interface bonds and sulfur(S)-oxygen(O) repulsion is found responsible for the decreased adhesion after S segregation. Interstitial and vacancy mediated S diffusion and the effects of Hf and Pt on the diffusion rate of S in bulk NiAl are studied. Hf is shown to reduce the diffusion rate, and the preferred diffusion mechanism of S and the influence of Pt are revealed to be temperature dependent. Finally, the effects of reactive elements on alumina grain boundary strength are studied. Reactive elements are shown to improve both the sliding and cleavage resistance, and the analysis of atomic separations suggest an increased ductility after the addition of quadrivalent Hf and Zr to the alumina grain boundaries.

Turbine components operate under severe loading conditions and at high and varying temperatures that result in thermal stresses in the presence of temperature gradients created by hot gases and cooling air. Moreover, static and cyclic loads as well as the motion of rotating components create mechanical stresses. The combined effect of complex thermo-mechanical stresses promote nucleation and propagation of cracks that give rise to fatigue and creep failure of the turbine components. Therefore, the relationship between thermo-mechanical stresses, chemical composition, heat treatment, resulting microstructure, operating temperature, material damage, and potential failure modes, i.e. fatigue and/or creep, needs to be well understood and studied. Artificial neural networks are promising candidate tools for such studies. They are fast, flexible, efficient, and accurate tools to model highly non-linear multi-dimensional relationships and reduce the need for experimental work and time-consuming regression analysis. Therefore, separate neural network models for γ’ precipitate strengthened Ni based superalloys have been developed for predicting the γ’ precipitate size, thermal expansion coefficient, fatigue life, and hysteresis energy. The accumulated fatigue damage is then estimated as the product of hysteresis energy and fatigue life. The models for γ’ precipitate size, thermal expansion coefficient, and hysteresis energy converge very well and match experimental data accurately. The fatigue life proved to be the most challenging aspect to predict, and fracture mechanics proved to potentially be a necessary supplement to neural networks. The model for fatigue life converges well, but relatively large errors are observed partly due to the generally large statistical variations inherent to fatigue life. The deformation mechanism map for 1.23Cr-1.2Mo-0.26V rotor steel has been constructed using dislocation glide, grain boundary sliding, and power law creep rate equations. The constructed map is verified with experimental data points and neural network results. Although the existing set of experimental data points for neural network modeling is limited, there is an excellent match with boundaries constructed using rate equations which validates the deformation mechanism map.

The structure and spatial arrangement of grain boundaries strongly affect the properties of polycrystalline materials such as corrosion, creep, weldability, superconductivity, and diffusivity. However, constructing predictive grain boundary structure-property models is taxing, both experimentally and computationally due to the high dimensionality of the grain boundary character space. The purpose of this work is to develop an effective method to infer grain boundary structure-property models from measurement of the effective properties of polycrystals by utilizing the inverse problem theory. This study presents an idealized case in which structure-property models for grain boundary diffusivity are inferred from a noisy simulation. The method presented in this study is derived from a general mathematical expression of inverse problem theory. The derivation of the method is carried step by step by considering diffusivity as the property of interest. The use of the Bayesian probability approach in the inference method makes the uncertainty quantification possible to perform. This study demonstrates how uncertainty quantification for the inferred structure-property models is easily performed within the idealized case framework. The method of quantifying the uncertainty is carried by utilizing the Metropolis-Hastings algorithm and Kernel Density Estimation method. The validation of the method is carried out by considering structure-property models with one, three, and five degrees of freedom. Two- and three-dimensional simulated polycrystals are used in this study to obtain the simulation data. The two-dimensional simulated polycrystals used in this study are generated using grain growth simulation performed using a front-tracking algorithm. The three-dimensional polycrystals used in this study are generated using Neper software resulting in a real-like polycrystals. The structure-property models used in the validation are picked by considering the qualitative features that reflect trends observed in literature. The inference method is performed by ignoring any knowledge about the structure-property model in the process.

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Here we investigate the organic ionic plastic crystal (OIPC) 1,2-bis[N-(N\'-hexylimidazolium-d2(4,5))]C2H4 2PF6- in one of its solid plastic crystal phases by means of multi-nuclear solid-state (SS) NMR and pulsed-field-gradient (PFG) NMR. We quantify distinct cation and anion diffusion coefficients as well as the diffusion activation energies (Ea) in this dicationic imidazolium-based OIPC. Our studies suggest a change in transport mechanism for the cation upon varying thermal and magnetic treatment (9.4 T), evidenced by changes in cation and anion Ea. Moreover, variable temperature 2H SSNMR lineshapes further support a change in local molecular environment upon slow cooling in B0. Additionally, we quantify the percentage of mobile anions as a function of temperature from variable temperature 19F SSNMR, where two distinct spectral features are present. We also comment on the pre-exponential factor (D0), giving insight into the number of degrees of freedom for both cation and anion as a function of thermal treatment. In conjunction with previously reported conductivity values for this class of OIPCs and the Stokes-Einstein relation, we propose that ion conduction is dominated by anion diffusion between crystallites (i.e., grain boundaries). Using our experimentally determine diffusion coefficient and previously reported PF6- hydrodynamic radius (rH), viscous (" = 4.1 Pa " s) ionic liquid (IL) is present with a cation rH of 0.34 nm. NMR measurements are very powerful in elucidating fundamental OIPC properties and allow a deeper understanding of ion transport within such materials. / Master of Science

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第17956号 / 工博第3804号 / 新制||工||1582(附属図書館) / 30786 / 京都大学大学院工学研究科材料工学専攻 / (主査)教授 辻 伸泰, 教授 松原 英一郎, 教授 安田 秀幸 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19308号 / 工博第4105号 / 新制||工||1633(附属図書館) / 32310 / 京都大学大学院工学研究科材料工学専攻 / (主査)教授 辻 伸泰, 教授 白井 泰治, 教授 乾 晴行 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Cross-correlation based analysis of electron backscatter diffraction (EBSD) patterns and the use of simulated reference patterns has opened up entirely new avenues of insight into local lattice properties within EBSD scans. The benefits of accessing new levels of orientation resolution and multiple types of previously inaccessible data measures are accompanied with new challenges in characterizing microscope geometry and other error previously ignored in EBSD systems. The foremost of these challenges, when using simulated patterns in high resolution EBSD (HR-EBSD), is the determination of pattern center (the location on the sample from which the EBSD pattern originated) with sufficient accuracy to avoid the introduction of phantom lattice rotations and elastic strain into these highly sensitive measures. This dissertation demonstrates how to greatly improve pattern center determination. It also presents a method for the extraction of grain boundary plane information from single two-dimensional surface scans. These are accomplished through the use of previously un-accessed detail within EBSD images, coupled with physical models of the backscattering phenomena. A software algorithm is detailed and applied for the determination of pattern center with an accuracy of ~0.03% of the phosphor screen width, or ~10µm. This resolution makes it possible to apply a simulated pattern method (developed at BYU) in HR-EBSD, with several important benefits over the original HR-EBSD approach developed by Angus Wilkinson. Experimental work is done on epitaxially-grown silicon and germanium in order to gauge the precision of HR-EBSD with simulated reference patterns using the new pattern center calibration approach. It is found that strain resolution with a calibrated pattern center and simulated reference patterns can be as low as 7x10-4. Finally, Monte Carlo-based models of the electron interaction volume are used in conjunction with pattern-mixing-strength curves of line scans crossing grain boundaries in order to recover 3D grain boundary plane information. Validation of the approach is done using 3D serial scan data and coherent twin boundaries in tantalum and copper. The proposed method for recovery of grain boundary plane orientation exhibits an average error of 3 degrees.