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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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Markl, Matthias
Friedrich-Alexander-Universität Erlangen-Nürnberg
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (20/20 displayed)
- 2024A Scan Strategy Based Compensation of Cumulative Heating Effects in Electron Beam Powder Bed Fusion
- 2024A CALPHAD-Informed Enthalpy Method for Multicomponent Alloy Systems with Phase Transitionscitations
- 2024Correction: A Scan Strategy Based Compensation of Cumulative Heating Effects in Electron Beam Powder Bed Fusion
- 2024Effect of Scanning Strategies on Grain Structure and Texture of Additively Manufactured Lattice Struts: A Numerical Explorationcitations
- 2024Numerical Microstructure Prediction for Lattice Structures Manufactured by Electron Beam Powder Bed Fusioncitations
- 2024Comprehensive numerical investigation of laser powder bed fusion process conditions for bulk metallic glassescitations
- 2023High-Throughput Numerical Investigation of Process Parameter-Melt Pool Relationships in Electron Beam Powder Bed Fusioncitations
- 2023Geometrical Influence on Material Properties for Ti6Al4V Parts in Powder Bed Fusioncitations
- 2023A Ray Tracing Model for Electron Optical Imaging in Electron Beam Powder Bed Fusioncitations
- 2023Revealing bulk metallic glass crystallization kinetics during laser powder bed fusion by a combination of experimental and numerical methodscitations
- 2023Numerical Design of CoNi-Base Superalloys With Improved Casting Structurecitations
- 2023Evaluation of Additively-Manufactured Internal Geometrical Features Using X-ray-Computed Tomographycitations
- 2022Basic Mechanism of Surface Topography Evolution in Electron Beam Based Additive Manufacturingcitations
- 2022Predictive simulation of bulk metallic glass crystallization during laser powder bed fusioncitations
- 2021Numerical Alloy Development for Additive Manufacturing towards Reduced Cracking Susceptibilitycitations
- 2021A Novel Approach to Predict the Process-Induced Mechanical Behavior of Additively Manufactured Materialscitations
- 2021How electron beam melting tailors the Al-sensitive microstructure and mechanical response of a novel process-adapted y-TiAl based alloycitations
- 2020Modeling and Simulation of Microstructure Evolution for Additive Manufacturing of Metals: A Critical Reviewcitations
- 2019MultOpt++: a fast regression-based model for the development of compositions with high robustness against scatter of element concentrationscitations
- 2018Development of Single-Crystal Ni-Base Superalloys Based on Multi-criteria Numerical Optimization and Efficient Use of Refractory Elementscitations
Places of action
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article
MultOpt++: a fast regression-based model for the development of compositions with high robustness against scatter of element concentrations
Abstract
lloys-by-design is a term used to describe new alloy development techniques based on numerical simulation. These approaches are extensively used for nickel-base superalloys to increase the chance of success in alloy development. During alloy production of numerically optimized compositions, unavoidable scattering of the element concentrations occurs. In the present paper, we investigate the effect of this scatter on the alloy properties. In particular, we describe routes to identify alloy compositions by numerical simulations that are more robust than other compositions. In our previously developed alloy development program package MultOpt, we introduced a sensitivity parameter that represents the influence of alloying variations on the final alloy properties in the post-optimization process, because the established sensitivity calculations require high computational effort. In this work, we derive a regression-based model for calculating the sensitivity that only requires one-time calculation of the regression coefficients. The model can be applied to any function with nearly linear behavior within the uncertainty range. The model is then successfully applied to the computational alloys-by-design work flow to facilitate alloy selection using the sensitivity of a composition owing to the inaccuracies in the manufacturing process as an additional minimization goal.