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1.
Vpliv izdelovalnih parametrov na lastnosti izdelkov iz Ti-6Al-4V, narejenih s selektivnim laserskim taljenjem in plastenje površine z bioaktivnim polimerom
Snehashis Pal, 2019, doktorska disertacija

Opis: Technological parameters included in energy density (ED) are the more powerful tools in selective laser melting (SLM) technology which can be used in the time of fabrication to regulate chemical, metallurgical, and mechanical properties of a product. The volumetric Energy Density (ED) depends on the energy input employed by the laser power, scanning speed, hatch spacing, and the layer thickness. Density, microstructure, surface morphology, dimension accuracy, strength and porosity including the number of pores, place of the pore, size of a pore shape of a pore, inclusions of pores of an SLM product depends on the processing parameters. As the powder material fusion process is done by track by track and layer by layer, the architecture of the microstructure in a product is oriented as the direction of building up too. The research has emphasized on metallurgical properties, tensile properties, and producing the non-porous products from Ti-6Al-4V alloy powder and surface modification using bioactive polymer for orthopedic application. The research has followed four steps to study the metallurgical properties and finding out the combinations of technological parameters in producing non-porous products. The purpose of the first step of the study was to examine the effects of ED on the product properties and to obtain an optimum ED as well as the optimal range of scanning speed. The second step of the study has focused on the influences of laser power. The third step of the study has investigated the effect of amounts of track overlapping and hatch spacing. Almost a zero-porosity product has been able to produce by following these three steps of the ongoing research. The fourth step has studied the metallurgical properties emphasizing on re-melting of every layer. High-density products have been found in the fourth step where a small amount of very small sized pores are present as a result of keyhole effect and gaseous bubble entrapment mainly. Four buildup orientations have been selected for each ED in the first step of the study to examine the tensile properties of the products. The best buildup orientation has been seen in longitudinally vertical tensile specimens considering tensile properties. The tensile properties have also been studied in the second and third step of the study with best build up orientation of the tensile specimens. The alterations of metallurgical and tensile properties have also been investigated after heat-treatment of the specific samples. Dimensional accuracies were also invigilated on the cubic, and tensile specimens over the studies and consequently, inaccuracies have been noticed. The fifth step of the study has observed the pore properties, adhesion properties, the compressive strength of gelatin coating manufactured using unidirectional freezing and the freeze-drying process of three different gelatin concentrations on four different surfaced Ti-6Al-4V alloy substrates. The results indicate that the coating properties depend on the substrate’s surface texture as well as the concentration of gelatin. Above 80% of porosity, interconnected and well-aligned pores of 75-200 μm have been obtained which is required to stimulate bone ingrowth histologically.
Ključne besede: selective laser melting, unidirectional freezing, fabricating parameters, porosity, microstructure, mechanical strength
Objavljeno: 01.04.2019; Ogledov: 737; Prenosov: 96
.pdf Celotno besedilo (10,52 MB)

2.
Intelligent system for prediction of mechanical properties of material based on metallographic images
Matej Paulič, David Močnik, Mirko Ficko, Jože Balič, Tomaž Irgolič, Simon Klančnik, 2015, izvirni znanstveni članek

Opis: This article presents developed intelligent system for prediction of mechanical properties of material based on metallographic images. The system is composed of two modules. The first module of the system is an algorithm for features extraction from metallographic images. The first algorithm reads metallographic image, which was obtained by microscope, followed by image features extraction with developed algorithm and in the end algorithm calculates proportions of the material microstructure. In this research we need to determine proportions of graphite, ferrite and ausferrite from metallographic images as accurately as possible. The second module of the developed system is a system for prediction of mechanical properties of material. Prediction of mechanical properties of material was performed by feed-forward artificial neural network. As inputs into artificial neural network calculated proportions of graphite, ferrite and ausferrite were used, as targets for training mechanical properties of material were used. Training of artificial neural network was performed on quite small database, but with parameters changing we succeeded. Artificial neural network learned to such extent that the error was acceptable. With the oriented neural network we successfully predicted mechanical properties for excluded sample.
Ključne besede: artificial neural network, factor of phase coherence between the surfaces, fracture toughness, image processing, mechanical properties, metallographic image, ultimate tensile strength, yield strength
Objavljeno: 12.07.2017; Ogledov: 671; Prenosov: 328
.pdf Celotno besedilo (2,02 MB)
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3.
Weldability of microalloyed high strength steels TStE 420 and S960QL
Marko Dunđer, Tomaž Vuherer, Ivan Samardžić, 2014, izvirni znanstveni članek

Opis: The paper presents research of weldability of microalloyed high strength steels TStE 420 and S960QL after weld thermal cycle simulation. Beside mechanical properties hardness and impact strength, the microstructures of characteristic weld thermal cycled structures for both steels are given. The results will contribure to determination of weakest points in Heat Affected Zone of both steels.
Ključne besede: weldabilty, high strength steels, mechanical properties, TStE 420, S960QL
Objavljeno: 03.07.2017; Ogledov: 1526; Prenosov: 91
.pdf Celotno besedilo (1,68 MB)
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