693.932 PEOPLE
| 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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Steinbach, Ingo
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (48/48 displayed)
- 2024Highly complex materials processes as understood by phase-field simulations
- 2024Automated Workflow for Phase‐Field Simulations: Unveiling the Impact of Heat‐Treatment Parameters on Bainitic Microstructure in Steelcitations
- 2024Coupling of alloy chemistry, diffusion and structure by grain boundary engineering in Ni–Cr–Fecitations
- 2024Multi-phase-field approach to fracture demonstrating the role of solid-solid interface energy on crack propagation
- 2023Coherency loss marking the onset of degradation in high temperature creep of superalloyscitations
- 2023Solidification of the Ni-based superalloy CMSX-4 simulated with full complexity in 3-dimensionscitations
- 2023Influence of Transformation Temperature on the High‐Cycle Fatigue Performance of Carbide‐Bearing and Carbide‐Free Bainitecitations
- 2023Phase-Field Study of the History-Effect of Remelted Microstructures on Nucleation During Additive Manufacturing of Ni-Based Superalloyscitations
- 20233D phase-field simulations to machine-learn 3D information from 2D micrographscitations
- 2022Microstructure property classification of nickel-based superalloys using deep learningcitations
- 2022Recent advances in understanding diffusion in muti-principal element systems
- 2022Recent Advances in Understanding Diffusion in Multiprincipal Element Systemscitations
- 2022Schmid rotations during high temperature creep in Ni-based superalloys related to coherency losscitations
- 2021Numerical study of epitaxial growth after partial remelting during selective electron beam melting in the context of Ni–Alcitations
- 202045-degree rafting in Ni-based superalloys citations
- 2020Multi-phase-field simulation of microstructure evolution in metallic foams
- 2018Development of Single-Crystal Ni-Base Superalloys Based on Multi-criteria Numerical Optimization and Efficient Use of Refractory Elementscitations
- 2016Atomistically informed extended Gibbs energy description for phase-field simulation of tempering of martensitic steel
- 2016Microstructure design of tempered martensite by atomistically informed full-field simulation
- 2015Primary combination of phase-field and discrete dislocation dynamics methods for investigating athermal plastic deformation in various realistic Ni-base single crystal superalloy microstructurescitations
- 2015Primary combination of phase-field and discrete dislocation dynamics methods for investigating athermal plastic deformation in various realistic Ni-base single crystal superalloy microstructurescitations
- 2014DFT-supported phase-field study on the effect of mechanically driven fluxes in Ni4Ti3 precipitation
- 2012Microsegregation and secondary phase formation during directional solidification of the single-crystal Ni-based superalloy LEK94citations
- 2010Modelling of hot ductility during solidification of steel grades in continuous castingcitations
- 2010Phase-field model with plastic flow for grain-growth in nanocrystalline materialcitations
- 2010Modelling of hot ductility during solidification of steel grades in continuous casting : part II
- 2010Modeling of hot ductility during solidification of steel grades in continuous castingcitations
- 2010Modeling of hot ductility during solidification of steel grades in continuous casting : part I
- 2009On the formation and growth of Mo-rich Laves phase particles during long-term creep of a 12% chromium tempered martensite ferritic steel
- 2009Upgrading CALPHAD to microstructure simulationcitations
- 2009Modeling of Microstructure Evolution during Solidification Processing
- 2009Numerical determination of heat distribution and castability simulations of as cast Mg-Al alloys
- 2008Direct Modeling of Structure Formation
- 2007The influence of lattice strain on pearlite formation in Fe-C
- 2007Simulation of microstructure evolution during solidification of magnesium-based alloys
- 2007Phase-field simulation of cooperative growth of pearlite
- 2006The role of carbon diffusion in ferrite on the kinetics of cooperative growth of pearlite : a multi-phase field study
- 2006The role of carbon diffusion in ferrite on the kinetics of cooperative growth of pearlitecitations
- 2006Phase field simulations of microstructure evolution during solidification of magnesium-based alloys
- 2006Multi phase field model for solid state transformation with elastic strain
- 2006Controlling microstructure in magnesium alloys : a combined thermodynamic, experimental and simulation approach
- 2006Controlling microstructure in magnesium alloyscitations
- 2004Lamellar pattern formation during 2-D-directional solidification of ternary eutectic alloys
- 2003Simulation of proeutectoid ferrite precipitation during technical heat treatment
- 2003Phase-field simulation of microstructure formation during directional solidification of a tenary eutectic alloy
- 2003The effect of thermodynamics and kinetics on the dendritic structure in tenary Fe-C-Mn
- 2000Structural supercooling in directional single crystal solidification : an experimental and numerical study
- 2000Structural supercooling in directional single crystal solidification
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article
Microstructure property classification of nickel-based superalloys using deep learning
Abstract
<jats:title>Abstract</jats:title><jats:p>Nickel-based superalloys have a wide range of applications in high temperature and stress domains due to their unique mechanical properties. Under mechanical loading at high temperatures, rafting occurs, which reduces the service life of these materials. Rafting is heavily affected by the loading conditions associated with plastic strain; therefore, understanding plastic strain evolution can help understand these material’s service life. This research classifies nickel-based superalloys with respect to creep strain with deep learning techniques, a technique that eliminates the need for manual feature extraction of complex microstructures. Phase-field simulation data that displayed similar results to experiments were used to build a model with pre-trained neural networks with several convolutional neural network architectures and hyper-parameters. The optimized hyper-parameters were transferred to scanning electron microscopy images of nickel-based superalloys to build a new model. This fine-tuning process helped mitigate the effect of a small experimental dataset. The built models achieved a classification accuracy of 97.74% on phase-field data and 100% accuracy on experimental data after fine-tuning.</jats:p>