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The machining process determines the overall quality of produced forming and forging dies, including surface integrity. Previous research found that surface integrity has a significant influence on the fatigue life of the dies. This thesis aims to establish a cost-effective approach for precision milling to obtain forming and forging dies with good surface integrity and long fatigue life. It combined experimental study accompanied by Finite Element Modelling and Artificial Intelligence soft modelling to predict and enhance forming and forging die life. Four machining parameters, namely Surface Speed, Depth of cut, Feed Rate and Tool Lead Angle, each with five levels, were investigated experimentally using Design of Experiment. An ANOVA analysis was carried out to identify the key factor for every Surface Integrity (SI) parameter and the interaction of every factor. It was found that the cutting force was mostly influenced by the tool lead angle. The residual stress and microhardness were both significantly influenced by the surface speed. However, on the surface roughness it was found that the feed rate had the most influence. After the machining experiments, four-point bending fatigue tests were carried out to evaluate the fatigue life of precision milled parts at an elevated temperature in a low cycle fatigue set-up imitated for the forming and forging production. It was found that surface roughness and hardness were the most influential factors for fatigue life. A 3D-FE-Modelling framework including a new material model subroutine was developed; this led to a more comprehensive material model. A fractional factorial simulation with over 180 simulations was carried out and validated with the machining experiment. Based on the experimental and simulation results, a soft prediction model for surface integrity was established by using Artificial Neural Networks (ANN) approach. These predictions for SI were then used in a Genetic Algorithm model to optimise the SI. The confirmation tests showed that the machining strategy was successfully optimised and the average fatigue duration was increased by at least a factor of two. It was found that a surface speed of 270 m/min, a feed rate of 0.0589 mm/tooth, a depth of cut of 0.39 mm and a tool lead angle of 16.045° provided the good surface integrity and increased fatigue performance. Overall, these findings conclude that the fundamentals and methodology utilised have developed a further understanding between machining and forming/forging process, resulting in a good foundation for a framework to generate FE and soft prediction models which can be used to in optimisation of precision milling strategy for different materials.

As a stem cell divides, two daughter cells are produced that either possess stem cell characteristics or become a cell with a more specialized function. Cellular markers have been identified that can be used to describe the phenotype of stem cell progeny. However, the development of tools that allow the description of cellular phenotypes remains a challenge due to the complexity of cellular heterogeneity. In my thesis, I address this problem by developing a phenotyping platform that stimulates cells to either remain in a stem cell state or become a cell with a more specialized func-tion. Multi-well plates were used to create microenvironments that resemble the natural surroundings of stem cells in tissues. High-resolution imaging technology was used to capture the cellular state after exposure to these microenvironments. High-content analysis was applied to generate a detailed description of every cell in the assay. Subsequently, mathematical modelling was used to describe the cell phenotyping profile in each environment. These profiles were then compared to characterise the cellular response to each environment. High-throughput technology was used to understand how topography features affect proliferation of pluripotent stem cells in a xeno-free environment. An algorithm was used to predict whether any given topography will support the pluripotent state. A second algorithm was used to predict an optimised topography. In the final part of my thesis, primary mouse skin fibroblasts and neural progenitor cells that were derived from human iPS cells were exposed to extrinsic cues in cellular assays. While distinct fibroblast subpopulations from neonatal mouse back skin responded to the assay irrespective of media conditions, human iPS cell-derived neural progenitor cells required the ad-dition of extrinsic cues to induce a self-renewal response. Therefore, it is possible to produce neural progenitor cells in vitro and study stem cell characteristics that are known to occur in an in vivo context.