| 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
The aim of this paper is to present the possibility of the application of Artificial Intelligence for determining fracture toughness and fatigue limit values of some grades of stainless steel. Experimental procedures for both, fracture toughness and fatigue limit determination are time-consuming, thus the application of artificial intelligence instead of long, time-exhausting experiments could result in less time spent waiting on experimental results as well as less resources that need to be provided. For this purpose, two Artificial Neural Networks (ANN) with same architecture (Fig. 1) were created and applied. The above mentioned properties are determined for the austenitic stainless steel X5CrNiMo17-12- 2 and X6Cr17 ferritic stainless steels. Complete work regarding ANN was conducted in Mathworks MATLAB 2017 software using nntool module. After completing training of ANN when adequate regression levels were reached, simulations were conducted using chemical composition of X5CrNiMo17- 12-2 and X6Cr17 steels. Obtained results are displayed in Fig. 2 and were compared with existing data. Conclusion that was drawn is that ANN that predicts KIC values has greater precision than ANN for fatigue limit. Potential reason for that could be that input layer needs more input data to increase precision. ; Published