Tamás Sándor

The Experiences of Activated Tungsten Inert Gas (ATIG) Welding Applied on 1.4301 Type Stainless Steel Plates

The TIG welding has a high disadvantage against the substantially high productivity welding procedures. This is why there were continuously going on several trials to improve the productivity of the TIG welding. The Activated Tungsten Inert Gas welding (ATIG welding) is one of these trials. Application experiments of ATIG welding on austenitic stainless steel plates will be presented. The main problems which appear when using the ATIG welding are the choosing of tungsten electrode, the suggested fitting of parts for the joining, portioning of the activating flux and the sensitivity for the measure of flux. They are extremely important to apply the ATIG welding and the results will be presented by this work. In the second half of this paper the comparison of the productivity differences between TIG and ATIG welding; mechanical properties, microstructure and corrosion resistance of welded joints will continue. Finally the evaluating the results and trying to classify the ATIG welding amongst the modern and most productive welding procedures is detailed. Furthermore advises for using the ATIG welding for suitable applications are represented.

ATIG Welding of Duplex Steel

The ATIG welding which is the high productivity version of conventional TIG welding, parallel with productivity increasing has some advantageous effect both on HAZ and weld joint grain structure. In this paper the effect of ATIG welding on austenite/ferrite ratio in duplex steel will be represented with experimental results performed on 8 mm thick 2205 type duplex steel. As collateral benefit through these results new evidences are originated to prove the “reversed Marangoni effect” theory.

Real time RHEED evaluation with the help of image processing

In this paper, the reflection high-energy electron diffraction (RHEED) pattern during the nano structure formation with the help of image processing is investigated. Nowadays, the growth of self-organised nano structures has been intensively investigated. It is very important to understand their growth process and the knowledge about their shape is particularly significant. The growth of these nano structures can be tracked in-situ manner with the help of RHEED. In-situ information about the stage of the process and the shape of the structure has been provided by RHEED. The temporal relation between the formation of the nano structure and the RHEED pattern is rather complicate. The image processing of the RHEED pattern help us to recognize and to interpret the metamorposis of the pattern during the growth process.

Soft-computing based classification and design of quantum dot nanostructures on GaAs substrate

The parameters of the semiconductor devices can be improved by nanostructures significantly. For this reason, it is necessary to produce nanostructures with given parameters. The soft-computing design of the self-organized nanostructures and a new classification model will be discussed in this paper. These nanostructures are formed by droplet epitaxy on compound semiconductor substrate. The parameters of the nanostructures (type, size, distribution) depend on the applied technology. The key factors of the technology (substrate temperature, Ga flux, As pressure, annealing time and annealing temperature) will be determined as design parameters. These parameters are set in order to produce nanostructures with the desired property. The revised version of previously introduced nanostructure fuzzy-based classification model will be also discussed.

Fuzzy and Kohonen SOM based classification of different 0D nanostructures

In this paper, the clustering of the GaAs-based droplet epitaxially grown self-assembled nanostructures was investigated by soft-computing methods. The properties and the operation of these devices, depend on the type, the shape, the size, and their distribution of these 0 dimensional nanostructures. Because of this, it is very important to know, how and what kind of nanostructures can form, at the given technological parameters. Our goal is the classification of these nanostructures, in order to support the research and the production of these devices. Our solution is based on the shape factor calculation of the given nanostructure. In this work, two possible classification methods of nanostructures were introduced as well. First, the classification potential of the Kohonen Self-Organizing Mapping (SOM) was investigated. Second, the fuzzy inference system based classification was studied. In this case, the shape factor was determined by geometrical sizes of the nanostructures. In this paper the clustering was introduced, which supports many kinds of technology as well.

Nanostructure Growth Supported by In Situ RHEED Evaluation

The in-situ monitoring of the MBE grown nanostructures can be carried out using the RHEED method. During the droplet epitaxal growth, the observation of the nanostructure formation is very important to understand the growth kinetics. In the present work, a novel in-situ RHEED evaluation and further MBE related developments are introduced, with which the quality of the nanostructure preparation can be improved.