Engineers constantly seek advancements in the performance of aircraft and power generation engines, including, lower costs and emissions, and improved fuel efficiency. Nickel-base superalloys are the material of choice for turbine discs, which experience some of the highest temperatures and stresses in the engine. Engine performance is proportional to operating temperatures. Consequently, the high-temperature capabilities of disc materials limit the performance of gas-turbine engines. Therefore, any improvements to engine performance necessitate improved alloy performance. In order to take advantage of improvements in high-temperature capabilities through tailoring of alloy microstructure, the overall objectives of this work were to establish relationships between alloy processing and microstructure, and between microstructure and mechanical properties. In addition, the project aimed to demonstrate the applicability of neural network modeling to the field of Ni-base disc alloy development and behavior. A full program of heat-treatment, microstructural quantification, mechanical testing, and neural network modeling was successfully applied to next generation Ni-base disc alloys. Mechanical testing included hot tensile, hot hardness, creep deformation, creep crack growth, and fatigue crack growth. From this work the mechanisms of processing-structure and structure-property relationships were studied. Further, testing results were used to demonstrate the applicability of machine-learning techniques to the development and optimization of this family of superalloys.

Ni-base superalloys are widely used in hot sections of gas turbine engines due to the high resistance to fatigue and creep at elevated temperatures. Due to the demands for improved performance and efficiency in applications of the superalloys, new and improved higher temperature alloy systems are being developed. Constitutive relations for these materials need to be formulated accordingly to predict behavior of cracks at notches in components under cyclic loading with peak dwell periods representative of gas turbine engine disk materials. Since properties are affected by microstructure at various length scales ranging from 10 nm tertiary γ' precipitates to 5-30 μm grains, hierarchical multiscale modeling is essential to address behavior at the component level. The goal of this work is to develop a framework for hierarchical multiscale modeling network that features linkage of several fine scale models to incorporate relevant microstructure attributes into the framework to improve the predictability of the constitutive model. This hierarchy of models is being developed in a collaborative research program with the Ohio State University. The fine scale models include the phase field model which addresses dislocation dissociation in the γ matrix and γ' precipitate phases, and the critical stresses from the model are used as inputs to a grain scale crystal plasticity model in a bottom-up fashion. The crystal plasticity model incorporates microstructure attributes by homogenization. A major task of the present work is to link the crystal plasticity model, informed by the phase field model, to the macroscale model and calibrate models in a top-down fashion to experimental data for a range of microstructures of the improved alloy system by implementing a hierarchical optimization scheme with a parameter clustering strategy. Another key part of the strategy to be developed in this thesis is the incorporation of polycrystal plasticity simulations to model a large range of virtual microstructures that have not been experimentally realized (processed), which append the experimentally available microstructures. Simulations of cyclic responses with dwell periods for this range of virtual (and limited experimental) polycrystalline microstructures will be used to (i) provide additional data to optimize parameter fitting for a microstructure-insensitive macroscopic internal state variable (ISV) model with thermal recovery and rate dependence relevant to the temperatures of interest, and (ii) provide input to train an artificial neural network that will associate the macroscopic ISV model parameters with microstructure attributes for this material. Such microstructure sensitive macroscopic models can then be employed in component level finite element studies to model cyclic behavior with dwell times at smooth and cracked notched specimens.

Nickel-base superalloys obtain high temperature mechanical properties through formation of precipitate phases formed via heat treatment. The precipitate microstructure evolves with heat treatment or thermal exposure, which can lead to degrading mechanical properties. This project focuses on the use of electrical resistivity as a non-destructive testing method to monitor the precipitate phase in Waspaloy (a polycrystalline nickel-base superalloy). The evolution of the precipitate microstructure is characterized throughout the volume of the specimens using both small angle neutron scattering (SANS) and ultra small angle X-ray scattering (USAXS) measurements. These measurements are also aided by microscopy and X-ray diffraction measurements.

No description available.

Thesis (M. App. Sc.)--Carleton University, 2004. / Includes bibliographical references (p. 99-114). Also available in electronic format on the Internet.

Thesis (M.App.Sc.) - Carleton University, 2005. / Includes bibliographical references (p. 57-63). Also available in electronic format on the Internet.

Thesis (M.App.Sc.) - Carleton University, 2006. / Includes bibliographical references (p. 110-113). Also available in electronic format on the Internet.

The goal of this work is to establish a life prediction methodology for thermomechanical loading of the Ni-base superalloy Mar-M247 over a larger temperature range than previous work. The work presented in this thesis extends the predictive capability of the Sehitoglu-Boismier unified thermo-viscoplasticity constitutive model and thermomechanical life model from a maximum temperature of 871C to a maximum temperature of 1038C. The constitutive model, which is suitable for predicting stress-strain history under thermomechanical loading, is adapted and calibrated using the response from isothermal cyclic experiments conducted at temperatures from 500C to 1038C at different strain rates with and without dwells. In the constitutive model, the flow rule function and parameters as well as the temperature dependence of the evolution equation for kinematic hardening are established. In the elevated temperature regime, creep and stress relaxation are critical behaviors captured by the constitutive model. The life model accounts for fatigue, creep, and environmental-fatigue damage under both isothermal and thermomechanical fatigue. At elevated temperatures, the damage terms must be calibrated to account for thermally activated damage mechanisms which change with increasing temperature. At lower temperatures and higher strain rates, fatigue damage dominates life prediction, while at higher temperatures and slower strain rates, environmental-fatigue and creep damage dominate life prediction. Under thermomechanical loading, both environmental-fatigue and creep damage depend strongly on the relative phasing of the thermal and mechanical strain rates, with environmental-fatigue damage dominating during out-of-phase thermomechanical loading and creep damage dominating in-phase thermomechanical loading. The coarse-grained polycrystalline microstructure of the alloy studied causes a significant variation in the elastic response, which can be linked to the crystallographic orientation of the large grains. This variation in the elastic response presents difficulties for both the constitutive and life models, which depend upon the assumption of an isotropic material. The extreme effects of a large grained microstructure on the life predictions is demonstrated, and a suitable modeling framework is proposed to account for these effects in future work.

In this research, controlled Waspaloy microstructures were produced with the objective of studying microstructural evolution in this alloy via electrically-based ac/dc non-destructive techniques. Correlations were developed between electrical measurements and alternate characterization techniques such as Ultra Small Angle X-ray Scattering (USAXS), Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD) to gain a complete understanding of the microstructural transformations and the associated mechanisms. Three different sets of controlled microstructures were produced in this research. In Set I microstructures, matrix (gamma) grain sizes of 13, 52 and 89 micrometers were obtained after solution-treatments at 1045 and 176;C, 1090 and 176;C and 1145 and 176;C respectively. A vacancy stabilization treatment at 1045 and 176;C followed after which, the specimens were aged at 800 and 176;C for times ranging from 0.1 hrs to 100 hrs to vary the gamma prime precipitate size distribution. In Sets II and III, the solution-treatment was only conducted at 1145 and 176;C, with the stabilization treatment conducted only in Set II. Subsequently, aging experiments were conducted at 725 and 176;C (or 700 and 176;C in Set II), 800 and 176;C and 875 and 176;C for times up to 100 hrs. DC four-point probe resistivity of specimens increased to a maximum upon initial aging from the solution-treated condition and showed a decreasing trend thereafter with successive aging. This, in addition to complementary evidence from SEM and USAXS, led to the conclusion that gamma prime nucleation-growth was complete by the time the resistivity maximum was observed. Resistivity variations that ensued upon successive aging after the maximum were attributed to microstructural/compositional changes due to gamma prime coarsening. The height of the maximum decreased drastically with increase in aging temperature from 725 and 176;C to 800 and 176;C, while the resistivity did not increase from the solution-treated condition upon aging at 875 and 176; C. Coarsening studies based on USAXS analysis indicated an LSW type volume diffusion mechanism of coarsening in Waspaloy, with an average coarsening rate constant of 3.25x10-29 [m3/sec] for Set I specimens aged at 800 and 176;C. Analytical and Finite Element (FE) models of two-probe impedance and dc four-point probe resistivity methods were developed to gain insight into the measured response and the accurate determination of material properties. AFM-based localized electrical examination of sub-grain Waspaloy microstructures was successfully conducted using electrostatic force microscopy (EFM), scanning Kelvin probe microscopy (SKPM) and current-AFM (I-AFM) electrical modes. I-AFM experiments revealed that the conductivity of the gamma prime phase was lower than that of the gamma phase.

This thesis involves the development of digital algorithms for the microstructural analysis of metallic deposits produced through the use of Scanning Laser Epitaxy (SLE). SLE is a new direct digital manufacturing (DDM) technique which allows for the creation of three dimensional nickel-based superalloy components using an incremental layering system. Using a bed of powder placed on an underlying substrate and a laser propagating a melt-pool across the sample, a layer of material can be added and through the careful control of SLE settings various microstructures can be created or extended from the substrate. To create parts that are within specified microstructure tolerances the ideal SLE settings must be located through experimental runs, with each material needing different operating parameters. This thesis focuses on improving the microstructural analysis by use of a program that tracks various features found in samples produced through the SLE technique and a data analysis program that provides greater insights into how the SLE settings influence the microstructure. Using this program the isolation of optimal SLE settings is faster while also providing greater insights into the process than is currently possible. The microstructure recognition program features three key aspects. The first evaluates major characteristics that typically arise during the SLE process; such as sample deformation, the aspects of a single crystal deposit, and the total deposit height. The second saves the data and all relevant test settings in a format that will allow for future analysis and comparison to other samples. Finally, it features a robust yet rapid execution so it may be used for entire runs of SLE samples, which can number up to 25, within a week. The program is designed for the types of microstructure found in CMSX-4 and Rene-80, specifically single crystal and equiaxed regions. The data fitting program uses optimally piecewise-fitted equations to find relationships between the SLE settings and the microstructure traits. The data is optimally piecewise fitted as the SLE process is a two-stage procedure, establishing then propagating the melt-pool across a sample, which creates distinct microstructure transitions. Using the information gathered, graphs provide a visual aid to better allow the experimenter to understand the process and a DOE is performed using sequential analysis; allowing the previously run samples to influence the future trials, reducing the amount of materials used while still providing great insight into the parameter field. Having access to the microstructure data across the entire sample and an advanced data fitting program that can accurately relate them to the SLE settings allows the program to track and optimize features that were never before possible.