| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
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
| Organizations | Location | People |
|---|
conferencepaper
Assessment of mechanical properties of austenitic stainless steels using artificial neural networks
Abstract
Knowledge of material properties is of key importance when planning the production of a product. This also applies to steel structures. Therefore, for the correct planning of a certain steel part or the production of a structure, it is necessary to get acquainted with the properties of the material, in order to make the correct decision about which material should be used. Bearing in mind that the volume of production of steel products is constantly increasing in various branches of industry and engineering, the problem of predicting the material properties needed to meet the requirements for efficient and reliable functioning of a certain part becomes imperative in the design process. In this research, a method for predicting four material characteristics (yield stress, tensile strength, elongation and hardness) for two stainless steels, using an artificial neural network (ANN), is presented. These material properties were predicted based on the known chemical compositions of the analyzed steels and the corresponding material properties available in the Cambridge Educational System EDU PACK 2010 software, using the neural network module of the MathWorks Matlab software package. The method was verified by comparing the material property values predicted by this method with the known property values for two analyzed stainless steels: X5CrNi18-10 (AISI 304) and X5CrNiMo17-12-2 (AISI 316). The difference between the two sets of values was less than 5%, and in some cases even negligible, which indicates the possibilities for the application of new technologies for predicting material properties. ; Published