Tube extrusion of hexagonal metals

Zr-2.5 wt % Nb (Zr-2.5Nb) is the main alloy used in the pressure tubes of CANDU nuclear reactors, which are manufactured by hot extrusion. Pressure tubes are subjected to high irradiation fields and corrosion, in addition to the applied stress at operating temperatures of around 330°C, which leads to irradiation creep that is often life-limiting; re-tubing the reactors, is a source of significant through-life cost of the reactor system. However, significant variability in performance is observed between tubes and stations, which is felt to be due to variability in the fabrication and operation conditions. The performance of Zr-2.5Nb is sensitive to both microstructure and texture and therefore it is desirable to be able to understand the extrusion conditions more fully. In this thesis, the extrusion of Zr-2.5Nb is examined, along with commercially pure titanium (CP Ti), commercially pure magnesium (CP Mg) and AA2014. The effect of extrusion ratio, die geometry and rod versus tube conditions are examined. The resulting microstructures and textures are rationalised with the aid of a finite element model for the process. After the introduction and literature review (Chapters 1-2), the modelling procedure and extrusion theory are examined (Chapter 3). Constitutive data (including friction conditions) are gathered and a Norton-Hoff constitutive model is generated in Chapter 4. It is found that adiabatic heating can be important at high strain rates and low temperatures, particularly in CP Ti and CP Mg. Recrystallization during deformation can be observed in the flow curves, particularly in CP Mg and AA2014 at low strain rates and high temperatures. The extrusion of AA2014 tubes is examined in Chapter 5. It is found that satisfactory textures and microstructures can be obtained, and that the model can reproduce the observed load curves. Partially extruded gridded billets are also used to verify the flow conditions predicted by the model and to obtain textures and microstructures part-way through the extrusion process. The extrusion of CP Mg and CP Ti are examined in Chapters 6 and 7, respectively. It was found that CP Mg recrystallized very easily, dominating the microstructures and textures observed. The CP Ti extrusions were performed in the [alpha]+[beta] regime in order to match Zr-2.5Nb conditions. The high extrusion ratio rod textures were dominated by the [beta]->[alpha]transformation, while those in the tubes were more characteristic of deformation of the [alpha] phase. Zr-2.5Nb extrusion is examined in Chapter 8. Satisfactory microstructures with elongated grains surrounded by thin ligaments of [beta] were obtained in the tube extruded through a flat-faced die, with the expected texture for this ratio of wall to diametral reduction (paragraph 2.5.7.1, Figure 2.25). The microstructures obtained were found to be a product of the temperature in the die and the cooling rate of the material. Excessive cooling rates lead to the production of basket-weave microstructures, and breakup of the grain boundary [alpha] to very fine microstructures. Again, the extrusion modelling allowed the results obtained to be rationalised. Finally, the reader is referred in Chapter 9 for a discussion of the obtained result. Conclusions drawn and suggestions for further work can be found in Chapter 10, together with recommendations for the industrial modelling of tube extrusion and for industrial practice

A combined machine learning and residual analysis approach for improved retrieval of shallow bathymetry from hyperspectral imagery and sparse ground truth data

Mapping shallow bathymetry by means of optical remote sensing has been a challenging task of growing interest in recent years. Particularly, many studies exploit earlier empirical models together with the latest multispectral satellite imagery (e.g., Sentinel 2, Landsat 8). However, in these studies, the accuracy of resulting bathymetry is (a) limited for deeper waters (>15 m) and/or (b) is being influenced by seafloor type albedo. This study explores further the capabilities of hyperspectral satellite imagery (Hyperion), which provides several spectral bands in the visible spectrum, along with existing reference bathymetry. Bathymetry predictors are created by applying the semi-empirical approach of band ratios on hyperspectral imagery. Then, these predictors are fed to machine learning regression algorithms for predicting bathymetry. Algorithm performance is being further compared to bathymetry predictions from multiple linear regression analysis. Following the initial predictions, the residual bathymetry values are interpolated by applying the Ordinary Kriging method. Then, the predicted bathymetry from all three algorithms along with their associated residual grids is used as predictors at a second processing stage. Validation results show that by using a second stage of processing, the root-mean-square error values of predicted bathymetry is being improved by ≈1 m even for deeper water (up to 25 m). It is suggested that this approach is suitable for (a) contributing wide-scale, high-resolution shallow bathymetry toward the goals of the Seabed 2030 program and (b) as a coarse resolution alternative to effort-consuming single-beam sonar or costly airborne bathymetric laser surveying

Packaging case studies - folding or set up?

Thesis (M.B.A.)--Boston University, 1949. This item was digitized by the Internet Archive