Métallurgie | EPFL Graph Search

vignette|Métallurgiste travaillant près des hauts-fourneaux. vignette|Ouvriers de la métallurgie dans les années 1920. La métallurgie est la science des matériaux qui étudie les métaux, leurs élaborations, leurs propriétés, leurs traitements. Par extension, on désigne ainsi l'industrie qui repose sur la maîtrise de cette science : de la fabrication des métaux et de leurs alliages, jusqu'à celle des biens qui en découlent. Définitions Une discipline qui se développe avec l'industrie La définition de la métallurgie a largement évolué au cours du . À partir des forgerons et leur accumulation de connaissances empiriques, la métallurgie est devenue au une science et dans le contexte de la révolution industrielle (et dans l'inconscient collectif), devenue synonyme d'acier, de hauts fourneaux, de laminoirs, de tréfileries, pour devenir ensuite une activité industrielle intense qui se préoccupe aussi de solutions de pointe, d'alliages spéciaux pour l'aéronautique, l'él

Experimental and numerical study of microstructure formation and the origin of crystallographic misorientation in Al-Zn-Si alloy coatings

Christoph Niederberger

Al-Zn-Si alloy coatings are widely used for the corrosion protection of steel sheets. In addition to the favorable corrosion properties, the Al-43wt%Zn-1.6wt%Si coatings present several features which are of metallurgical interest. The first one is their surface appearance dominated by the typical spangle morphology exhibiting dull and shiny areas on the coating surface. Further on, the dendrite tips do apparently not grow along the 〈100〉 crystallographic directions that are typical for fcc-metals, but rather along directions in between 〈100〉 and 〈110〉. In addition, continuous variations of the crystallographic orientation, up to 35°, are observed within individual grains of Al-Zn-Si coatings [Sémoroz1 01]. The objective of the present study is to establish a better understanding of the microstructure development which is responsible for the formation of both the characteristic spangle pattern and the important variations of crystallographic orientation within the grains. In order to elucidate these questions, the present study includes three main axes, (i) a detailed microstructure characterization of industrially solidified samples, (ii) modeling work which encompasses microstructure modeling by the phase field method and a geometrical model, as well as the determination of the solid-liquid interfacial energy anisotropy by an inverse method, and (iii) re-solidification experiments aimed at studying the behavior of Al-Zn-Si layers under modified solidification conditions. The results show that the dendrite network spreads quickly in the coating layer at a temperature between 530 to 535°C. During growth, the dendrite tips are separated from the confining boundaries by a thin, solute-rich liquid film. It was found that the preferred dendrite growth directions are in between 〈100〉 and 〈110〉, 28.5° from 〈100〉. Further on, a mathematical expression for the interfacial energy anisotropy of the considered alloy has been determined. The combination of the geometrical model and surface topography measurements allowed concluding that the spangle pattern is due to preferential dendrite growth along one of the two boundaries confining the melt layer. In addition, the new experimental evidence forced to discard the mechanisms previously proposed for the formation of intragranular crystallographic misorientations. The experimental findings acquired during this study indicate that the solidification shrinkage occurring in the area of the grain envelope is the driving force for the formation of the observed intragranular misorientations. The solidification shrinkage leads to the development of tensile stresses in the oxide film covering the coating while it solidifies. These stresses apply on the dendrite network and lead to plastic deformation in the tip area of the growing dendrite arms.

EPFL2007

Machine learning for metallurgy IV: A neural network potential for Al-Cu-Mg and Al-Cu-Mg-Zn

William Curtin, Daniel John Gilles Marchand

Most metallurgical properties, e.g., dislocation propagation, precipitate formation, can only be fully understood atomistically but most phenomena and quantities of interest cannot be measured experimentally. Accurate simulation methods are essential but first-principles density functional theory (DFT) is prohibitively expensive while empirical interatomic potentials are rarely sufficiently accurate for alloys. Machine learning (ML) is emerging as an approach to create computationally-efficient atomistic potentials achieving near-DFT accuracy. Building on recent work on binary Al-Cu and ternary Al-Mg-Si, here a family of neural network potentials (NNPs) for Al alloys of Al-Cu-Mg and Al-Cu-Mg-Zn is developed and assessed using the Behler-Parinello formulation. Training of the potentials uses a robust set of metallurgically-relevant structures including intermetallic phases, stacking faults, solute/solute and solute/stacking fault interactions, solute clusters, and matrix/precipitate interfaces. The accuracy of these NNPs is then demonstrated across a comprehensive set of properties derived from the training set structures and, moreover, many important structures not represented in the training set such as the generalized stacking fault energy (GSFE) surface of the critical S-phase precipitate in Al-Mg-Cu, and antisite and vacancy formation energies for Al-Cu-Mg intermetallics. The broader Al-Cu-Mg-Zn NNPs also have high accuracy for subtle properties such as Cu substitutional energies in the n' and T phases and formation energies of small Al-Zn-Mg clusters. Together with earlier results, this paper shows how increasingly complex multicomponent alloy potentials can be systematically developed by expanding a training database, leading to a comprehensive set of potentials for a broad alloy family, demonstrated here for the technological Al-2xxx, Al-5xxx, and Al-7xxx Al alloys.

AMER PHYSICAL SOC2022

Neural Network Potentials for Age Hardening Aluminum Alloys

Daniel John Gilles Marchand

High-strength metal alloys achieve their performance via careful control of precipitates and solutes.The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic scale phenomena, particularly during early-stage nucleation and growth. Atomistic modeling using interatomic potentials is a desirable tool for understanding the detailed phenomena involved in precipitation and strengthening, which requires length and time scales far larger than those accessible by first-principles methods. Current interatomic potentials for alloys are not, however, sufficiently accurate for such studies.In this work we will consider new interatomic potentials for the 2xxx (Al-Cu-Mg), 6xxx (Al-Mg-Si), and 7xxx (Al-Zn-Mg-Cu) age-hardenable aluminum alloys. First a family of neural-network potentials (NNPs) for the Al-Cu system are presented as a simple example of a machine-learning potential that can achieve near-first-principles accuracy for many different metallurgically-important aspects of 2xxx alloys. High fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, generalized stacking fault energies and surfaces for slip in matrix and precipitates, antisite defect energies, and other quantities, are shown. The NNPs also captures the subtle entropically-induced transition between

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and

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at temperatures around 600K. Many comparisons are made with the state-of-the-art Angular-Dependent Potential for Al-Cu, demonstrating the significant quantitative benefit of a machine-learning approach. A preliminary kinetic Monte Carlo study shows the NNP to predict the emergence of GP zones in Al-4at%Cu at T=300K in agreement with experiments.Then, a family of NNPs for the Al-Mg-Si system is developed to enable quantitative studies of 6xxx alloys. The NNP is trained on metallurgically-important quantities computed by first principles density function theory (DFT) leading to high fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, Al stacking fault energies, antisite defect energies, and other quantities. The Generalized Stacking Fault Energy surfaces (GSFE) for the three prevalent

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precipitate compositions in peak-aged Al-6xxx are then computed with the NNP, and are validated by DFT computations at key points. A preliminary examination of early-stage clustering kinetics and energetics in Al-6xxx is then made, showing the formation of low-energy Mg-Si structures and the trapping of vacancies in these clusters.Finally we demonstrate a family of NNPs to produce what we believe to be the first Al-Cu-Mg (2xxx) and Al-Cu-Mg-Zn (7xxx) potentials of sufficient accuracy to be used for atomistic simulations. These NNP studies show that the NNP has significant transferability to defects and properties outside the structures used to train the NNP, but also shows some errors highlighting that the use of any interatomic potential requires careful validation in application to specific metallurgical problems of interest.

EPFL2022