Properties of highly ordered crystalline materials, like strength and ductility, are dependent on the preferred orientations of the grains within the material, that is, the texture. When a material is processed and microstructural transformations occur, the texture of the material changes drastically. These texture evolutions are presently simulated using Crystal Plasticity Finite Element Methods (CPFEM). While these simulations are precise, they are computationally expensive and slow.

A surrogate for such simulation methods that can benefit from a data-driven approach could be deep learning through artificial neural networks. The aim of this study is to leverage a suitable deep learning neural network model and assess its ability to capture the complexity of texture. As such, Normalizing Flows (NF), a generative deep learning model, is employed to learn textures by capturing the multi-modal distribution of discrete sets of orientation matrices that represent those textures. The performance of the model is assessed for two types of data, matrices that belong to a fixed texture (unconditional modelling) and matrices paired with simple conditions (conditional modelling) that produce textures based on the conditions. The training data for the neural network is synthetically generated in a microstructure modelling software. The model is trained on this synthetic data and subsequently used to generate samples on the distribution it has learned. These samples and ground truth data are plotted in pole figures to compare the distribution of the generated data and the ground truth data, respectively, to visualize the model’s generative capability.

It is found that the model closely approximates the distribution of a given texture, capturing the structure of the distribution, albeit having a reduced density in the modes of the distribution. The conditional model is also capable of generating the approximate texture relevant to the condition given to it during evaluation. These findings indicate that the model is capable of learning texture, and further improvements in the model’s architecture could make the model highly robust.

This thesis aims to investigate both the two-dimensional and three-dimensional (sub)structure of the butterfly martensite (α’BF) morphology. In order to do this, multiple different types of heat treatments were applied to a Fe-25Ni alloy to find the optimal morphology consisting of a low density of α’BF that is surrounded by austenite(γ). Two dimensional analyses were performed by applying a combination of optical microscopy, scanning electron microscopy and Electron Backscatter Diffraction (EBSD). The α’BF morphology was found to nucleate and grow as the first martensite (α’) morphology just below the martensite start temperature (Ms) within this 25Ni alloy. A decrease in austenisation time resulted in smaller γ grain sizes. This reduction in γ grain size resulted in a reduction of the Ms of the alloy which in its turn reduced the amount of undercooling applied to the material below Ms upon quenching towards room temperature, causing a decrease in the freshly formed α’ fraction. Upon observation of the freshly formed α’ it was found that the α’BF seemed to prefer formation near the centre of γ grains instead of near γ grain boundaries. When α’BF was formed near a γ grain boundaries, one wing tends to aligned itself with this boundary. Through trace analysis, it was found that the habit planes of α’BF were close to {557}γ, {225}γ and {3 10 15}γ, which are characteristic habit planes of lath, butterfly and lenticular α’, respectively. The orientation relationship (OR) between the γ and α’BF is found be a combination of both the Greninger-Troiano (G-T) and Nishiyama-Wasserman (N-W) OR. Three dimensional analysis was performed using both serial sectioning and 3D-EBSD. Serial sectioning showed that α’BF within this alloy was sensitive to formation upon mechanical polishing. 3D-EBSD gave insight on the three-dimensional morphology and substructure of the α’BF grains. It was observed that the junction plane could be non-continuous. Moreover, it is shown that the apparent wing angle greatly depends on the angle that the α’BF grain makes perpendicular to the sample surface and that the tail of the the α’BF grain can run along the entire length of the grain.

In our modern world, where computers, mobile phones and many other applications have become indispensable, there is a growing need for high precision machines in order to be able to produce these technologies. For these complex high precision applications it is important to understand the fatigue properties of the materials used to prevent premature failure, as these materials are subjected to large numbers of stress cycles. A material that is used for high precision applications is Ti-6Al-4V, as its material properties are highly adaptable and can be fine-tuned for a wide range of applications. Material fatigue due to stress cycles knows two stages: crack initiation and crack propagation. This research focuses on the latter and looks into the influence of microstructural features on crack propagation in Ti-6Al-4V. The influence of the microstructural features is tested by applying a load shedding method to form cracks in Ti-6Al-4V samples. These cracks are analysed with Scanning Electron Microscopy (SEM) and Electron BackScatter Diffraction (EBSD) in order to relate the microstructural features to the crack path. There are two main microstructural features found to have a large influence on the fatigue crack propagation in Ti-6Al-4V. The first of these is the Schmid factor, which relates the applied stress to the slip system available. As there are only a small amount of slip systems available in Ti-6Al-4V and limited crack path propagation opportunities, large crack deflections can be the result. The second microstructural feature found to have a large influence is the misorientation angle of the grain boundaries. When the misorientation angle is large enough, a shift from transgranular cracking to intergranular cracking is observed. Intergranular cracking can cause deviations of the crack path around the grains and the formation of secondary cracks. The deviations in crack path as a result of a low Schmid factor and a high misorientation angle extend the fatigue life of the material. The Schmid factor was found to have a large influence on crack deflections observed, whereas the high misorientation angles were mostly found around sites where bifurcation occurred, especially at lower applied stress ranges. This study proposes that the influence of the microstructural features on fatigue crack propagation in Ti-6Al-4V can be expressed as two probability functions describing crack deflection and bifurcation. The probability of a crack deflecting is relatively high for a lower Schmid factor, a high misorientation angle and a low deflection angle. The probability of bifurcation is relatively high when a near-threshold stress intensity range is applied and also for a high misorientation angle and a low deflection angle.