| 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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Altamirano-Guerrero, Gerardo
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 2021Optimal Design of Hot-Dip Galvanized DP Steels via Artificial Neural Networks and Multi-Objective Genetic Optimizationcitations
- 2019Microstructural Characterization of the Laser Welding in a Nickel Based Superalloycitations
- 2019Effect of Retained Austenite and Non-Metallic Inclusions on the Mechanical Properties of Resistance Spot Welding Nuggets of Low-Alloy TRIP Steelscitations
- 2019Study of Static Recrystallization Kinetics and the Evolution of Austenite Grain Size by Dynamic Recrystallization Refinement of an Eutectoid Steelcitations
- 2019Optimization of the Continuous Galvanizing Heat Treatment Process in Ultra-High Strength Dual Phase Steels Using a Multivariate Modelcitations
- 2018Experimental Determination of Continuous Cooling Transformation (CCT) Diagrams for Dual-Phase Steels from the Intercritical Temperature Rangecitations
- 2016Experimental Determination of Continuous Cooling Transformation Diagrams of Hot-Rolled Heat Treatable Steel Plates Using Quenching Dilatometrycitations
- 2015Influence of Boron on the Precipitation Kinetics in Advanced Ultra-High Strength Steelscitations
- 2012Effect of boron on the continuous cooling transformation kinetics in a low carbon advanced ultra-high strength steel (A-UHSS)citations
- 2012Dynamically recrystallized austenitic grain in a low carbon advanced ultra-high strength steel (A-UHSS) microalloyed with boron under hot deformation conditionscitations
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article
Optimal Design of Hot-Dip Galvanized DP Steels via Artificial Neural Networks and Multi-Objective Genetic Optimization
Abstract
This modeling and optimization study applies a non-linear back-propagation artificial neural network, commonly denoted as BPNN, to model the most important mechanical properties such as yield strength ( YS ), ultimate tensile strength ( UTS ) and elongation at fracture ( EL ) during the experimental processing of hot-dip galvanized dual-phase (GDP) steels. Once the non-linear BPNN is properly trained, the most important variables of the continuous galvanizing process, including initial/first cooling rate ( CR1 ), holding time at the galvanizing temperature of 460 °C ( tg ) and the final/second cooling rate ( CR2 ), are obtained in an optimal way using an evolutionary approach. The experimental development of GDP steels in continuous processing lines with outstanding mechanical properties (550 < YS < 750 MPa, 1100 MPa < UTS and 10% < EL ) is possible by using a combined hybrid approach based in BPNN and multi-objective genetic algorithm (GA). The proposed computational method is applied to the specific design of an actual manufacturing process for the first time.