Production of maraging steel grades and the influence of specified and nonspecified elements for special applications

The material properties of maraging steels are greatly affected by the alloy and inclusion content and hence by the production route. This paper describes various past and current production routes at Böhler Edelstahl GmbH and the effect of these routes on the specified element (alloy) and non-specified element (inclusion) contents. Non-metallic inclusions were investigated by EDX analysis. The effects of various alloying and tramp elements on the material properties are presented as a statistical evaluation of previous melts. Nitrogen solubility was calculated with Thermo–Calc for maraging steel Mat.-No. 1.6354 and the precipitation of nitrides is discussed. A proposal is made for the production of melts with improved properties.     

Electrochemical Faceting of polycrystalline platinum in HF solutions

The electrochemical faceting of polycrystalline (pc) platinum yielding a (100)-preferred oriented electrode surface was accomplished in HF solutions by applying to the pc platinum electrode a square wave potential perturbation at 1 kHz in the 0.2 to 1.3 V range (vs rhe). SEM patterns of the resulting surfaces were also obtained. For HF concentrations up to 2 M no appreciable influence of the electrolyte concentration on the electrochemical faceting was noticed. Otherwise, the voltammetric response of the (100)-type preferred oriented platinum surface in the H-adatom potential range when compared to previously reported data resulted remarkably sensitive to the electrolyte composition. The discussion of results was based on the non-equilibrium adsorption conditions for anions under the periodic perturbation.     

Mitteilungen der Gesellschaft für Informatik 194. Folge

Ohne Zusammenfassung     

3D Hair sketching for real-time dynamic & key frame animations

Physically based simulation of human hair is a well studied and well known problem. But the “pure” physically based representation of hair (and other animation elements) is not the only concern of the animators, who want to “control” the creation and animation phases of the content. This paper describes a sketch-based tool, with which a user can both create hair models with different styling parameters and produce animations of these created hair models using physically and key frame-based techniques. The model creation and animation production tasks are all performed with direct manipulation techniques in real-time.     

Epoxy paint failure in B-52 fuel tanks: Part II. Influence of DIEGME concentration in the fuel on the failure process

A model has been proposed to explain the failure of the original BMS10-39 epoxy paint on upper vertical surfaces in B-52 fuel tanks. The model involves interaction of the paint with DIEGME, a fuel system ice inhibitor (FSII) in jet fuel, that is distilled from the liquid fuel. In this communication, distillation experiments used to support the model are refined to better match the mass transfer of vapor from fuel in a B-52 fuel tank at close to room temperature. The interaction of these lower temperature distillates with the paint affirms the earlier model. On the basis of these experiments it is proposed that paint failure may be controlled or eliminated by reducing the level of DIEGME in the fuel. Proposed changes in military jet fuel composition are detailed.     

Integration of prior knowledge of measurement noise in kernel density classification

Samples can be measured with different precisions and reliabilities in different experiments, or even within the same experiment. These varying levels of measurement noise may deteriorate the performance of a pattern recognition system, if not treated with care. Here we seek to investigate the benefit of incorporating prior knowledge about measurement noise into system construction. We propose a kernel density classifier which integrates such prior knowledge. Instead of using an identical kernel for each sample, we transform the prior knowledge into a distinct kernel for each sample. The integration procedure is straightforward and easy to interpret. In addition, we show how to estimate the diverse measurement noise levels in a real world dataset. Compared to the basic methods, the new kernel density classifier can give a significantly better classification performance. As expected, this improvement is more obvious for small sample size datasets and large number of features.