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Selective laser melting of metals: desktop machines open up new chances even for small companies
(2012)
Additive manufacturing (AM) of metal parts by using Selective Laser Melting (SLM) has become a powerful tool mostly in the area of automotive, aerospace engineering and others. Especially in the field of dentistry, jewelry and related branches that require individualized or even one-of-a-kind products, the direct digital manufacturing process opens up new ways of design and manufacturing. In these fields, mostly small and medium sized businesses (SME) are operating which do not have sufficient human and economic resources to invest in this technology. But to stay competitive, the application of AM can be regarded as a necessity. In this situation a new desktop machine (Realizer SLM 50) was introduced that cost about 1/3 of a shop floor SLM machine and promises small quality parts. To find out whether the machine really is an alternative for SMEs the University of Applied Science, Aachen, Germany, designed, build and optimized typical parts from the dentistry and the jewelry branches using CoCr and silver material, the latter being new with this application. The paper describes the SLM procedure and how to find and optimize the most important parameters. The test is accompanied by digital simulation in order to verify the build parameters and to plan future builds. The procedure is shown as well as the resulting parts made from CoCr and silver material.
The aim of this work was to perform a detailed investigation of the use of Selective Laser Melting (SLM) technology to process eutectic silver-copper alloy Ag 28 wt. % Cu (also called AgCu28). The processing occurred with a Realizer SLM 50 desktop machine. The powder analysis (SEM-topography, EDX, particle distribution) was reported as well as the absorption rates for the near-infrared (NIR) spectrum. Microscope imaging showed the surface topography of the manufactured parts. Furthermore, microsections were conducted for the analysis of porosity. The Design of Experiments approach used the response surface method in order to model the statistical relationship between laser power, spot distance and pulse time.
Self-Adjoint Operator
(2009)
68Ga-radiopharmaceuticals are common in the field of Nuclear Medicine to visualize receptor-mediated processes. In contrast to straightforward labeling procedures for clinical applications, preclinical in vitro and in vivo applications are hampered for reasons like e.g. volume
restriction, activity concentration, molar activity and osmolality. Therefore, we developed a semiautomatic system specifically to overcome these problems. A difficulty appeared unexpectedly, as intrinsic trace metals derived from eluate (Zn, Fe and Cu) are concentrated as well in amounts that influence radiochemical yield and thus lower molar activity.
Semi-insulating GaAs layers grown by molecular-beam epitaxy / P. Kordos ; A. Förster ; J. Betko ...
(1995)
Based on an identifying Volterra type integral equation for randomly right censored observations from a lifetime distribution function F, we solve the corresponding estimating equation by an explicit and implicit Euler scheme. While the first approach results in some known estimators, the second one produces new semi-parametric and pre-smoothed Kaplan–Meier estimators which are real distribution functions rather than sub-distribution functions as the former ones are. This property of the new estimators is particular useful if one wants to estimate the expected lifetime restricted to the support of the observation time.
Specifically, we focus on estimation under the semi-parametric random censorship model (SRCM), that is, a random censorship model where the conditional expectation of the censoring indicator given the observation belongs to a parametric family. We show that some estimated linear functionals which are based on the new semi-parametric estimator are strong consistent, asymptotically normal, and efficient under SRCM. In a small simulation study, the performance of the new estimator is illustrated under moderate sample sizes. Finally, we apply the new estimator to a well-known real dataset.