| 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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Shirzadi, Amir A.
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (21/21 displayed)
- 2020Diffusion bonding of TiC or TiB reinforced Ti–6Al–4V matrix composites to conventional Ti–6Al–4V alloycitations
- 2019Development of Auto Ejection Melt Spinning (AEMS) and its application in fabrication of cobalt-based ribbonscitations
- 2019Layered Structures of Ti-6Al-4V Alloy and Metal Matrix Composites on Its Base Joint by Diffusion Bonding and Friction Weldingcitations
- 2019Modelling and design of new stainless-steel welding alloys suitable for low-deformation repairs and restoration processescitations
- 2019Mechanical Properties and Processing Techniques of Bulk Metal–Organic Framework Glassescitations
- 2019A new method for liquid-phase bonding of copper plates to aluminum nitride (AlN) substrates used in high-power modulescitations
- 2018Gallium-assisted diffusion bonding of stainless steel to titanium; microstructural evolution and bond strengthcitations
- 2016Effect of Cu addition on microstructure and impact toughness in the simulated coarse-grained heat-affected zone of high-strength low-alloy steelscitations
- 2015Microstructure and Interfacial Reactions During Vacuum Brazing of Stainless Steel to Titanium Using Ag-28 pct Cu Alloycitations
- 2015Austenite memory and variant selection in a novel martensitic welding alloycitations
- 2013Microstructure and interfacial reactions during active metal brazing of stainless steel to titaniumcitations
- 2012Effect of SiC reinforcement particles on the grain density in a magnesium-based metal-matrix composite: modelling and experimentcitations
- 2012Crystallization model of magnesium primary phase in the AZ91/SiC compositecitations
- 2011Combined effect of stress and strain on crystallographic orientation of bainite
- 2011Design of weld fillers for mitigation of residual stresses in ferritic and austenitic steel weldscitations
- 2010Neural network modelling of hot deformation of austenite
- 2010Comparison of alloying concepts for Low Transformation Temperature (LTT) welding consumables
- 2010Modelling of residual stress minimization through martensitic transformation in stainless steel welds
- 2009Stainless steel weld metal designed to mitigate residual stressescitations
- 2009Bainite orientation in plastically deformed austenitecitations
- 2008Joining ceramics to metals using metallic foamcitations
Places of action
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conferencepaper
Neural network modelling of hot deformation of austenite
Abstract
The hot deformation behaviour of austenite in steels is a complicated process which depends on chemical composition, microstructure, temperature and strain rate. While many models have been developed to represent the flow stress as a function of these variables, it is not yet possible to predict the behaviour for a new alloy. Linear regression techniques are not capable of representing the data, however, neural networks are capable of modelling highly non-linear data. A neural network model was developed in this work using a large database of various steels. The model allows the calculation of error bars that depend upon the position of a prediction in the input space and the level of perceived noise in the data. The validity of the model was evaluated by comparing its outputs against those of the six carbon-manganese steels with different compositions.