Modelling and Analysis of Solidification Shrinkage and Defect Prediction in Metals

The dendritic structure is one of the most complex forms of crystallisation in nature and technology. In constrained crystal growth conditions, under uniform stress, packing and microsegregation, dendrites grow aligned as antiparallel as possible to the heat flow. Such directionally solidified single crystal alloys find a wide range of applications, from semi-conductors, laser optics to applications in aerospace engines. The purpose of single crystal solidification is to eliminate detrimental grain boundaries that limit the creep ductility of the component, whilst orienting the crystal structure as parallel as possible to the maximum loading direction. However, the manufacture of single crystals of high quality and of practical size is no trivial task. This is especially the case for multicomponent alloys with many constituent elements where large convective forces, non-uniform stresses and macrosegregation are present within the melt, which results in inherent casting defects. To predict and control the final microstructure, the influence of multiple thermo-physical mechanisms on both local and bulk scale must be well understood. The present thesis is devoted to developing novel multiphysics models for the prediction and thus prevention of casting defects associated with single crystal growth. Research has shown that dendrites preferentially grow in the [001] direction, however, their growth direction may vary when influenced by non-uniform stress distribution (bending) and isotherm curvature. Currently, in computational solidification science, models do not take these into account thus fail to predict the complex variation in microstructural patterns. To solve this, a novel Fluid-Solid Interaction (FSI) formulation designed for multiphase flows is proposed and validated aiming to simulate stresses and strains in complex geometries evolving through the Phase Field (PF) method. The FSI-PF method is successfully applied to investigate stress during dendritic interface evolution revealing the potential mechanism behind defect formation and mosaicity. To further elucidate the mechanism behind thermal contraction induced bending of the (001) plane, pattern recognition, characterisation and machine learning algorithms are built. In this work, DenMap is introduced - an automatic pattern recognition tool developed for feature extraction and optimisation of single crystal analysis. Furthermore, a Shape-Limited Primary Spacing (SLPS) algorithm is established for rapid and accurate determination of packing patterns, defects, and local primary spacing distribution within bulk single crystal microstructures. Incorporated in this work is also a foundation for 3D crystal reconstruction methodologies, advanced 3D misalignment and time-of-flight energy-resolved neutron imaging techniques. These new standardised methods facilitate the bulk investigation of large-scale deformation mechanisms such as mosaicity, low angle grain boundaries (LABs), enabling further improvement in computational approaches.</p

On Directional Dendritic Growth and Primary Spacing—A Review

The primary spacing is intrinsically linked with the mechanical behavior of directionally solidified materials. Because of this relationship, a significant amount of solidification work is reported in the literature, which relates the primary spacing to the process variables. This review provides a comprehensive chronological narrative on the development of the directional dendritic growth problem over the past 85 years. A key focus within this review is detailing the relationship between key solidification parameters, the operating point of the dendrite tip, and the primary spacing. This review critiques the current state of directional dendritic growth and primary spacing modelling, briefly discusses dendritic growth computational and experimental research, and suggests areas for future investigation

Self-Assembly of Methanethiol on the Reconstructed AU(111) Surface

We present a combined experimental and theoretical study of molecular methanethiol (CH3 SH) adsorption on the reconstructed Au(111) surface in the temperature range between 90 and 300 K in UHV. We find that the simplest thiol molecules form two stable self-assembled monolayer (SAM) structures that are created by distinct processes. Below 120 K, a solid rectangular phase, preserving the herringbone reconstruction, emerges from individual chains of spontaneously formed dimers. At higher adsorption temperatures below 170 K, a close-packed phase forms via dissociative CH3 SH adsorption and the formation of Au adatoms that are not incorporated into the SAM. We show that the combination of a strong substrate-mediated interaction with nondissociative dimerization and temperature activated removal of the Au(111) reconstruction drives the large-scale assembly of molecular CH3 SH into two distinct phases

Self-organized nanotemplating on misfit dislocation networks investigated by scanning tunneling microscopy

Self-ordering growth of nanoarrays on strained metallic interfaces is an attractive option for preparing highly ordered nanotemplates. The great potential of this natural templating approach is that symmetry, feature sizes, and density are predicted to depend on the interfacial stress in these strained layers, which can be adjusted by changing the substrate-thin film composition, temperature, and adlayer coverage. This bottom-up approach of growing nanostructured twodimensional ordered arrays of clusters on the misfit dislocation networks of strained metallic thin films and surfaces requires a detailed understanding of the nucleation and film-adsorbate interaction processes. Here we show how high resolution, large scale, variable temperature scanning tunneling microscopy imaging can improve our understanding of these self-assembly processes

The Effect of Heat Source Path on Thermal Evolution during Electro-Gas Welding of Thick Steel Plates

In recent years, the shipbuilding industry has experienced a growing demand for tighter control and higher strength requirements in thick steel plate welding. Electro-gas welding (EGW) is a high heat input welding method, widely used to improve the welding efficiency of thick plates. Modelling the EGW process of thick steel plates has been challenging due to difficulties in accurately depicting the heat source path movement. An EGW experiment on 30 mm thickness E36 steel plates was conducted in this study. A semi-ellipsoid heat source model was implemented, and its movement was mathematically expressed using linear, sinusoidal, or oscillate-stop paths. The geometry of welding joints, process variables, and steel composition are taken from industrial scale experiments. The resulting thermal evolutions across all heat source-path approaches were verified against experimental observations. Practical industrial recommendations are provided and discussed in terms of the fusion quality for E36 steel plates with a heat input of 157 kJ/cm. It was found that the oscillate-stop heat path predicts thermal profile more accurately than the sinusoidal function and linear heat path for EGW welding of 30 mm thickness and above. The linear heat path approach is recommended for E36 steel plate thickness up to 20 mm, whereas maximum thickness up to 30 mm is appropriate for sinusoidal path, and maximum thickness up to 35 mm is appropriate for oscillate-stop path in EGW welding, assuming constant heat input

GAKTpore:stereological characterisation methods for porous foams in biomedical applications

In tissue engineering, scaffolds are a key component that possess a highly elaborate pore structure. Careful characterisation of such porous structures enables the prediction of a variety of large-scale biological responses. In this work, a rapid, efficient, and accurate methodology for 2D bulk porous structure analysis is proposed. The algorithm, “GAKTpore”, creates a morphology map allowing quantification and visualisation of spatial feature variation. The software achieves 99.6% and 99.1% mean accuracy for pore diameter and shape factor identification, respectively. There are two main algorithm novelties within this work: (1) feature-dependant homogeneity map; (2) a new waviness function providing insights into the convexity/concavity of pores, important for understanding the influence on cell adhesion and proliferation. The algorithm is applied to foam structures, providing a full characterisation of a 10 mm diameter SEM micrograph (14,784 × 14,915 px) with 190,249 pores in ~9 min and has elucidated new insights into collagen scaffold formation by relating microstructural formation to the bulk formation environment. This novel porosity characterisation algorithm demonstrates its versatility, where accuracy, repeatability, and time are paramount. Thus, GAKTpore offers enormous potential to optimise and enhance scaffolds within tissue engineering

On the origin of mosaicity in directionally solidified Ni-base superalloys

This article addresses the formation of low angle grain boundaries which give rise to mosaicity, a phenomenon which has only recently received attention in the single crystal Ni-base superalloy field. In this work, post-mortem advanced microscopy characterisation techniques are employed to deduce the dendrite tip growth kinetics from transverse sections of a single crystal turbine blade. As a result, it has been possible to highlight the role of isotherm curvature in inducing lateral macro-segregation parallel to a growing solidification front. Using crystallographic data from time-of-flight energy-resolved neutron imaging and novel Bragg-dip post processing, it is established that lateral macro-segregation induces small angle grain boundaries which gives rise to mosaicity within single crystal Ni-base superalloys. Mosaicity demonstrates good correlation the local primary spacing, where faster growing dendrites demonstrate greater deviation of from the growth axis, as compared with those growing at a slower rate. In light of these findings, the origin of mosaicity and its implication to secondary grain formation is discussed

GAKTpore: Stereological Characterisation Methods for Porous Foams in Biomedical Applications

In tissue engineering, scaffolds are a key component that possess a highly elaborate pore structure. Careful characterisation of such porous structures enables the prediction of a variety of large-scale biological responses. In this work, a rapid, efficient, and accurate methodology for 2D bulk porous structure analysis is proposed. The algorithm, “GAKTpore”, creates a morphology map allowing quantification and visualisation of spatial feature variation. The software achieves 99.6% and 99.1% mean accuracy for pore diameter and shape factor identification, respectively. There are two main algorithm novelties within this work: (1) feature-dependant homogeneity map; (2) a new waviness function providing insights into the convexity/concavity of pores, important for understanding the influence on cell adhesion and proliferation. The algorithm is applied to foam structures, providing a full characterisation of a 10 mm diameter SEM micrograph (14,784 × 14,915 px) with 190,249 pores in ~9 min and has elucidated new insights into collagen scaffold formation by relating microstructural formation to the bulk formation environment. This novel porosity characterisation algorithm demonstrates its versatility, where accuracy, repeatability, and time are paramount. Thus, GAKTpore offers enormous potential to optimise and enhance scaffolds within tissue engineering

GAKTpore: Stereological Characterisation Methods for Porous Metal Foams in Biomedical Applications- Data

In this work, a rapid, efficient, and accurate methodology for 2D bulk porous structure analysis and pore morphology classification is proposed. The algorithm, “GAKTpore”, creates a morphology map allowing visualisation of spatial feature variation, which enables optimisation of pore sizes/shapes/ranges/dispersion within any porous structure. The micrographs used in this study with their data outputted from the GAKTpore algorithm and below. Micrographs are stored as .tiff files and data is outputted and saved as a .CSV file