A5018395605- Microstructure and mechanical properties
- Aluminum Alloy Microstructure Properties
- High Temperature Alloys and Creep
- Advanced Materials Characterization Techniques
- Machine Learning in Materials Science
- Microstructure and Mechanical Properties of Steels
- Iron and Steelmaking Processes
- Metal Extraction and Bioleaching
- Solidification and crystal growth phenomena
- Metallurgical Processes and Thermodynamics
- Metallurgy and Material Forming
- Hydrogen embrittlement and corrosion behaviors in metals
- Advanced Electron Microscopy Techniques and Applications
- Numerical methods in engineering
- Composite Material Mechanics
- High-Velocity Impact and Material Behavior
- Force Microscopy Techniques and Applications
- Metal and Thin Film Mechanics
- Aluminum Alloys Composites Properties
- Electron and X-Ray Spectroscopy Techniques
- Elasticity and Material Modeling
- Rock Mechanics and Modeling
- Advanced materials and composites
- Minerals Flotation and Separation Techniques
- Composite Structure Analysis and Optimization
Max-Planck-Institut für Nachhaltige Materialien
2018-2024
RWTH Aachen University
2013-2021
Max Planck Society
2019-2021
École Polytechnique Fédérale de Lausanne
2019
Lawrence Berkeley National Laboratory
2019
University of California, Berkeley
2019
Ben-Gurion University of the Negev
2019
Sharif University of Technology
2011-2012
Abstract Single crystal Ni-based superalloys have long been an essential material for gas turbines in aero engines and power plants due to their outstanding high temperature creep, fatigue oxidation resistance. A turning point was the addition of only 3 wt.% Re second generation single which almost doubled creep lifetime. Despite significance this improvement, mechanisms underlying so-called “Re effect” remained controversial. Here, we provide direct evidence enrichment crystalline defects...
Abstract We propose a deep neural network (DNN) as fast surrogate model for local stress calculations in inhomogeneous non-linear materials. show that the DNN predicts stresses with 3.8% mean absolute percentage error (MAPE) case of heterogeneous elastic media and mechanical contrast up to factor 1.5 among neighboring domains, while performing 103 times faster than spectral solvers. The proves suited reproducing distribution geometries different from those used training. In elasto-plastic...
Fossil-free ironmaking is indispensable for reducing massive anthropogenic CO2 emissions in the steel industry. Hydrogen-based direct reduction (HyDR) among most attractive solutions green ironmaking, with high technology readiness. The underlying mechanisms governing this process are characterized by a complex interaction of several chemical (phase transformations), physical (transport), and mechanical (stresses) phenomena. Their interplay leads to rich microstructures, hierarchy defects...
Abstract The purpose of this work is the development a trained artificial neural network for surrogate modeling mechanical response elasto-viscoplastic grain microstructures. To end, U-Net-based convolutional (CNN) using results von Mises stress field from numerical solution initial-boundary-value problems (IBVPs) equilibrium in such microstructures subject to quasi-static uniaxial extension. resulting CNN (tCNN) accurately reproduces about 500 times faster than solutions corresponding IBVP...
We propose strategies that couple natural language processing with deep learning to enhance machine capability for corrosion-resistant alloy design. First, accuracy of models materials datasets is often limited by their inability incorporate textual data. Manual extraction numerical parameters from descriptions or experimental methodology inevitably leads a reduction in information density. To overcome this, we have developed fully automated approach transform data into form compatible...
Abstract Science is and always has been based on data, but the terms ‘data-centric’ ‘4th paradigm’ of materials research indicate a radical change in how information retrieved, handled performed. It signifies transformative shift towards managing vast data collections, digital repositories, innovative analytics methods. The integration artificial intelligence its subset machine learning, become pivotal addressing all these challenges. This Roadmap Data-Centric Materials explores fundamental...
Abstract The present work aims at the identification of effective constitutive behavior $$\Sigma 5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Σ</mml:mi><mml:mn>5</mml:mn></mml:mrow></mml:math> aluminum grain boundaries (GB) for proportional loading by using machine learning (ML) techniques. input ML approach is high accuracy data gathered in challenging molecular dynamics (MD) simulations atomic scale varying temperatures and conditions. traction-separation...
Abstract A seamless and lossless transition of the constitutive description elastic response materials between atomic continuum scales has been so far elusive. Here we show how this problem can be overcome by using artificial intelligence (AI). convolutional neural network (CNN) model is trained, taking structure image a nanoporous material as input corresponding elasticity tensor, calculated from molecular statics (MS), output. Trained with atomistic data, CNN captures size- pore-dependency...
Combined atomic-scale characterization and simulation reveal the complexity diversity of chemical nature dislocations.
Abstract We have developed a deep-learning-based framework for understanding the individual and mutually combined contributions of different alloying elements environmental conditions towards pitting resistance corrosion-resistant alloys. A fully connected deep neural network (DNN) was trained on previously published datasets corrosion-relevant electrochemical metrics, to predict potential an alloy, given chemical composition conditions. Mean absolute error 170 mV in predicted potential,...
Abstract The complex interplay between chemistry, microstructure, and behavior of many engineering materials has been investigated predominantly by experimental methods. Parallel to the increase in computer power, advances computational modeling methods have resulted a level sophistication which is comparable that experiments. At continuum level, one class such models based on thermodynamics, phase-field methods, crystal plasticity, facilitating account multiple physical mechanisms...
In the continuum context, displacements of atoms induced by a dislocation can be approximated disregistry field. this work, two phase-field (PF)-based approaches and their variants are employed to calculate fields static, extended dislocations pure edge screw character in face-centred cubic metals: Au Al, which have distinct stable stacking fault energy elastic anisotropy. A new truncated Fourier series form is developed approximate generalised (GSFE) surface, shows significant improvement...
Mixed-type dislocations are prevalent in metals and play an important role their plastic deformation. Key characteristics of mixed-type cannot simply be extrapolated from those with pure edge or screw characters. However, traditionally received disproportionately less attention the modeling simulation community. In this work, we explore core structures Al using three continuum approaches, namely, phase-field dislocation dynamics (PFDD) method, atomistic microelasticity (APFM) concurrent...