| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Arsić, Dušan
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (19/19 displayed)
- 2024Advanced welding technologies: FSW in automotive manufacturing
- 2024Influence of FDM printing parameters on the compressive mechanical properties and fracture behavior of ABS material
- 2024A new artificial neural network model for predicting fatigue limit and fracture toughness values of some stainless steels
- 2024A new artificial neural network model for predicting fatigue limit and fracture toughness values of some stainless steels
- 2024Assessment of mechanical properties of austenitic stainless steels using artificial neural networks
- 2024Predicting the yield stress and tensile strength of two stainless steel using artificial intelligence
- 2023Prediction of service life of components and structures of hydro power plants during the design, prototyping and service period
- 2023Influence of TiN Coating on the Drawing Force and Friction Coefficient in the Deep Drawing Process of AlMg4.5Mn0.7 Thin Sheetscitations
- 2023Effect of plastic strain and specimen geometry on plastic strain ratio values for various materialscitations
- 2022Theoretical-experimental estimation of weldability of different types of steels by hard facing
- 2022Analysis of Filler Metals Influence on Quality of Hard-Faced Surfaces of Gears Based on Tests in Experimental and Operating Conditionscitations
- 2021Influence of different hard-facing procedures on quality of surfaces of regenerated gearscitations
- 2020The effect of heat input on the fracture behaviour of surface weld metal of rail steel
- 2018Tribological characteristics of Al/SiC/Gr hybrid compositescitations
- 2018The influence of heat input on the toughness and fracture mechanism of surface weld metal
- 2018Working life estimate of the tubular T-joint by application of the LEFM concept
- 2018Experimental-numerical analysis of appearance and growth of a crack in hard-faced layers of the hot-work high-strength tool steels
- 2016COMPARATIVE STUDY OF AN ENVIRONMENTALLY FRIENDLY LUBRICANT WITH CONVENTIONAL LUBRICANTS IN STRIP IRONING TEST
- 2015Two-phase ironing process in conditions of ecologic and classic lubricants application
Places of action
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conferencepaper
A new artificial neural network model for predicting fatigue limit and fracture toughness values of some stainless steels
Abstract
Besides the modern non-metallic materials, which can successfully replace metallic materials in certain fields, steel materials are still largely present in technical practice. That trend will remain for many years to come. That is why there is a need to develop new types of steel, that possess better properties, in addition to the existing ones. The Cr-Mo steels, with a high vanadium content, belong to a group of the newer steels, with relatively high values of hardness and toughness. The X180CrMo12-1 steel, with varying percentages of vanadium, within the limits of 0.5-3 %, was used for the tests in this work. Vanadium, as a carbide-forming alloying element, creates a carbide network of the M7C3 type around the metal substrate, and finely dispersed carbides of the V6C5 type within the metal substrate. For the conducted research, modern equipment was used for analysis of the chemical composition, monitoring of the shape of metal grains and carbide network, tests of resistance to friction and wear, as well as for electrochemical characterization. In the conducted research, the objective was to determine the carbide composition, microstructure, and morphology and to evaluate their impact on the material's characteristics. The steel samples were experimentally examined using scanning electron microscopy with energy dispersive spectrometry (SEM-EDS) and Xray diffractometric analysis (XRD). The carbide composition analysis has confirmed that this actually was the M7C3 carbide, as it was earlier assumed. ; Published