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High chromium cast iron (HCCI) is considered as one of the most useful wear resistance materials and their usage are widely spread in industry. The wear resistance and mechanical properties of HCCI mainly depend on type, size, number, morphology of hard carbides and the matrix structure (γ or α). The Hypereutectic HCCI with large volume fractions of hard carbides is preferred to apply in wear applications. However, the coarser and larger primary M7C3 carbides will be precipitated during the solidification of the hypereutectic alloy and these will have a negative influence on the wear resistance. In this thesis, the Ti-added hypereutectic HCCI with a main composition of Fe-17mass%Cr-4mass%C is quantitatively studied based on the type, size distribution, composition and morphology of hard carbides and martensite units. A 11.2μm border size is suggested to classify the primary M7C3 carbides and eutectic M7C3 carbides. Thereafter, the change of the solidification structure and especially the refinement of carbides (M7C3 and TiC) size by changing the cooling rates and Ti addition is determined and discussed. Furthermore, the mechanical properties of hypereutectic HCCI related to the solidification structure are discussed. Mechanical properties of HCCI can normally be improved by a heat treatment process. The size distribution and the volume fraction of carbides (M7C3 and TiC) as well as the matrix structure (martensite) were examined by means of scanning electron microscopy (SEM) and electron backscattered diffraction (EBSD). Especially for the matrix structure, EBSD is a useful tool to classify the fcc (γ) and bcc (α) phases. In conclusion, low holding temperatures close to the eutectic temperature and long holding times are the best heat treatment strategies in order to improve wear resistance and hardness of Ti-alloyed hypereutectic HCCI. / <p>QC 20121130</p>

This thesis investigates some problems related to the form and shape of statistical distributions with the main focus on goodness of fit and bump hunting. A bump is a distinctive characteristic of distributional shape. A search for bumps, or bump hunting, in a probability density function (PDF) has long been an important topic in statistical research. We introduce a new definition of a bump which relies on the notion of the curvature of a planar curve. We then propose a new method for bump hunting which is based on a kernel density estimator of the unknown PDF. The method gives not only the number of bumps but also the location of their centers and base points. In quantitative risk applications, the selection of distributions that properly capture upper tail behavior is essential for accurate modeling. We study tests of distributional form, or goodness-of-fit (GoF) tests, that assess simple hypotheses, i.e., when the parameters of the hypothesized distribution are completely specified. From theoretical and practical perspectives, we analyze the limiting properties of a family of weighted Cramér-von Mises GoF statistics W2 with weight function psi(t)=1/(1-t)^beta (for beta<=2) which focus on the upper tail. We demonstrate that W2 has no limiting distribution. For this reason, we provide a normalization of W2 that leads to a non-degenerate limiting distribution. Further, we study W2 for composite hypotheses, i.e., when distributional parameters must be estimated from a sample at hand. When the hypothesized distribution is heavy-tailed, we examine the finite sample properties of W2 under the Chen-Balakrishnan transformation that reduces the original GoF test (the direct test) to a test for normality (the indirect test). In particular, we compare the statistical level and power of the pairs of direct and indirect tests. We observe that decisions made by the direct and indirect tests agree well, and in many cases they become independent as sample size grows. / Thesis / Doctor of Philosophy (PhD)

This dissertation consists of four independent but related parts, each in a Chapter. The first part is an introductory. It serves as the background introduction and offer preparations for later parts. The second part discusses two population multivariate normal distributions with common covariance matrix. The goal for this part is to derive objective/non-informative priors for the parameterizations and use these priors to build up constructive random posteriors of the Kullback-Liebler (KL) divergence of the two multivariate normal populations, which is proportional to the distance between the two means, weighted by the common precision matrix. We use the Cholesky decomposition for re-parameterization of the precision matrix. The KL divergence is a true distance measurement for divergence between the two multivariate normal populations with common covariance matrix. Frequentist properties of the Bayesian procedure using these objective priors are studied through analytical and numerical tools. The third part considers the star-shape Gaussian graphical model, which is a special case of undirected Gaussian graphical models. It is a multivariate normal distribution where the variables are grouped into one "global" group of variable set and several "local" groups of variable set. When conditioned on the global variable set, the local variable sets are independent of each other. We adopt the Cholesky decomposition for re-parametrization of precision matrix and derive Jeffreys' prior, reference prior, and invariant priors for new parameterizations. The frequentist properties of the Bayesian procedure using these objective priors are also studied. The last part concentrates on the discussion of objective Bayesian analysis for partial correlation coefficient and its application to multivariate Gaussian models. / Ph. D.

Three components, a pallet, packaging, and material handling equipment, of the unit load portion of the supply chain are physically and mechanically interacting during product storage and shipping. Understanding the interactions between two primary components, a pallet and packaging, in a unit load is a key step towards supply chain cost reduction and workplace safety improvement. Designing a unit load without considering physical and mechanical interactions, between those two components, can result in human injury or death caused from a unsafe workplace environment and increased supply chain operating costs, due to product damage, high packaging cost, disposal expense, and waste of natural resources. This research is directed towards developing predictive models of the compressive stress distributions using the principle of the beam on an elastic foundation and experimentally quantifying the compressive stress distributions. The overall objective of this study is to develop a model that predicts compressive stress distributions at the interface between a pallet deck and packaging as a function of: pallet deck stiffness, packaging stiffness, and pallet joint fixity. The developed models were validated by comparison to the results of physical testing of the unit load section. Design variables required for modeling included Modulus of Elasticity (MOE) of pallet deckboards, Rotation Modulus (RM) for nailed joints, and packaging stiffness. Predictive models of the compressive stress distributions were non-uniformly distributed across the interface between pallet deckboards and packaging. Maximum compressive stresses were observed at the deckboard ends over stringer segments. All predictive compressive stress distributions were influenced by pallet deck stiffness, packaging stiffness, and joint fixity. The less the joint fixity the greater the pallet deck deflection. The stiffer deckboards are more sensitive to joint fixity. For predictive compressive stress distribution models, the measure of the stress concentrations was the Compressive Stress Intensity Factor (SIF), which was the ratio of the estimated maximum compressive stress to the applied stress. Less stiff pallets and stiffer packaging resulted in greater SIF for all end condition models. SIF was reduced by stiffer joint, stiffer pallet deck and more flexible packaging. The stiffer the pallet deck and pallet joint the greater the effective bearing area. The lower stiffness packaging resulted in the greater effective bearing area with all three packages. The predicted effective bearing area was more influenced by pallet deck stiffness than the packaging stiffness. The developed prediction models were validated by comparison to experimental results. All prediction models fell within 95% confidence bounds except the 3/8-inch deck with free ends and 3/4-inch deck with fixed ends. The difference between predicted and measured results was due to a limitation in pressure sensor range and test specimen construction for the free end model and fixed end model, respectively. The results show effects of pallet deck stiffness and packaging stiffness on SIFs with percentage changes ranging from 2 to 26% (absolute value of change) for all three end conditions. The sensitivity study concluded that changing both pallet deck stiffness and packaging stiffness more significantly influenced the SIFs than bearing areas. / Ph. D.

Detection systems are widely used in industry. Designers, operators and users of these systems need to choose an appropriate design, based on the intended usage and the operating environment. The purpose of this research is to analyze the effect of various system design variables (controllable) and system parameters (uncontrollable) on the performance of detection systems. To optimize system performance one must manage the tradeoff between two errors that can occur. A False Alarm occurs if the detection system falsely indicates a target is present and a False Clear occurs if the detection system falsely fails to indicate a target is present. Given a particular detection system and a pre-specified false clear (or false alarm) rate, there is a minimal false alarm (or false clear) rate that can be achieved. Earlier research has developed methods that address this false alarm, false clear tradeoff problem (FAFCT) by formulating a Neyman-Pearson hypothesis problem, which can be solved as a Knapsack problem. The objective of this research is to develop guidelines that can be of help in designing detection systems. For example, what system design variables must be implemented to achieve a certain false clear standard for a parallel 2-sensor detection system for Salmonella detection? To meet this objective, an experimental design is constructed and an analysis of variance is performed. Computational results are obtained using the FAFCT-methodology and the results are presented and analyzed using ROC (Receiver Operating Characteristic) curves and an analysis of variance. The research shows that sample size (i.e., size of test data set used to estimate the distribution of sensor responses) has very little effect on the FAFCT compared to other factors. The analysis clearly shows that correlation has the most influence on the FAFCT. Negatively correlated sensor responses outperform uncorrelated and positively correlated sensor responses with large margins, especially for strict FC-standards (FC-standard is defined as the maximum allowed False Clear rate). Suggestions for future research are also included. FC-standard is the second most influential design variable followed by grid size. / Master of Science