Published May 2019
| Version v1
Journal article
Growth of β intermetallic in an Al-Cu-Si alloy during directional solidification via machine learned 4D quantification
Creators
- 1. School of Metallurgy and Materials, University of Birmingham (United Kingdom)
- 2. Centre for Numerical Modelling and Process Analysis, University of Greenwich (United Kingdom)
- 3. School of Mechanical Engineering, University College London (United Kingdom)
- 4. ESRF-The European Synchrotron, 71 Avenue des Martyrs, 38000 Grenoble (France)
- 5. School of Computer Science, University of Birmingham (United Kingdom)
- 6. Dept. of Materials Science and Engineering, McMaster University, Hamilton (Canada)
Description
Fe contamination is a serious composition barrier for Al recycling. In Fe-containing Al-Si-Cu alloy, a brittle and plate-shaped β phase forms, degrading the mechanical properties. Here, 4D (3D plus time) synchrotron X-ray tomography was used to observe the directional solidification of Fe-containing Al-Si-Cu alloy. The quantification of the coupled growth of the primary and β phase via machine learning and particle tracking, demonstrates that the final size of the β intermetallics were strongly influenced by the solute segregation and space available for growth whereas the β orientation was controlled by the temperature gradient direction. The work can be used to validate predictive models.
Additional details
Identifiers
- DOI
- 10.1016/j.scriptamat.2019.02.007;
- PII
- S135964621930079X;
Publishing Information
- Journal Title
- Scripta Materialia
- Journal Volume
- 165
- Journal Page Range
- p. 29-33
- ISSN
- 1359-6462
- CODEN
- SCMAF7
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 55043073
- Subject category
- S36: MATERIALS SCIENCE;
- Descriptors DEI
- DIFFUSION BARRIERS; INTERMETALLIC COMPOUNDS; MACHINE LEARNING; MECHANICAL PROPERTIES; SILICON ALLOYS; SOLIDIFICATION; SOLUTES; SYNCHROTRONS; TEMPERATURE GRADIENTS; TOMOGRAPHY; X RADIATION
- Descriptors DEC
- ACCELERATORS; ALGORITHMS; ALLOYS; ARTIFICIAL INTELLIGENCE; CYCLIC ACCELERATORS; DIAGNOSTIC TECHNIQUES; ELECTROMAGNETIC RADIATION; IONIZING RADIATIONS; LEARNING; MATHEMATICAL LOGIC; PHASE TRANSFORMATIONS; RADIATIONS
Optional Information
- Copyright
- Copyright (c) 2019 Published by Elsevier Ltd on behalf of Acta Materialia Inc. All rights reserved.