Crystal nucleation in metallic alloys using x-ray radiography and machine learning
The crystallization of solidifying Al-Cu alloys over a wide range of conditions was studied in situ by synchrotron x-ray radiography, and the data were analyzed using a computer vision algorithm trained using machine learning. The effect of cooling rate and solute concentration on nucleation underco...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
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American Association for the Advancement of Science
2018
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| _version_ | 1797066375931363328 |
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| author | Liotti, E Arteta, C Zisserman, A Lui, A Lempitsky, V Grant, P |
| author_facet | Liotti, E Arteta, C Zisserman, A Lui, A Lempitsky, V Grant, P |
| author_sort | Liotti, E |
| collection | OXFORD |
| description | The crystallization of solidifying Al-Cu alloys over a wide range of conditions was studied in situ by synchrotron x-ray radiography, and the data were analyzed using a computer vision algorithm trained using machine learning. The effect of cooling rate and solute concentration on nucleation undercooling, crystal formation rate, and crystal growth rate was measured automatically for thousands of separate crystals, which was impossible to achieve manually. Nucleation undercooling distributions confirmed the efficiency of extrinsic grain refiners and gave support to the widely assumed free growth model of heterogeneous nucleation. We show that crystallization occurred in temporal and spatial bursts associated with a solute-suppressed nucleation zone. |
| first_indexed | 2024-03-06T21:41:09Z |
| format | Journal article |
| id | oxford-uuid:47f7f437-94d9-43a9-b819-8d960994dfb9 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:41:09Z |
| publishDate | 2018 |
| publisher | American Association for the Advancement of Science |
| record_format | dspace |
| spelling | oxford-uuid:47f7f437-94d9-43a9-b819-8d960994dfb92022-03-26T15:23:02ZCrystal nucleation in metallic alloys using x-ray radiography and machine learningJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:47f7f437-94d9-43a9-b819-8d960994dfb9EnglishSymplectic Elements at OxfordAmerican Association for the Advancement of Science2018Liotti, EArteta, CZisserman, ALui, ALempitsky, VGrant, PThe crystallization of solidifying Al-Cu alloys over a wide range of conditions was studied in situ by synchrotron x-ray radiography, and the data were analyzed using a computer vision algorithm trained using machine learning. The effect of cooling rate and solute concentration on nucleation undercooling, crystal formation rate, and crystal growth rate was measured automatically for thousands of separate crystals, which was impossible to achieve manually. Nucleation undercooling distributions confirmed the efficiency of extrinsic grain refiners and gave support to the widely assumed free growth model of heterogeneous nucleation. We show that crystallization occurred in temporal and spatial bursts associated with a solute-suppressed nucleation zone. |
| spellingShingle | Liotti, E Arteta, C Zisserman, A Lui, A Lempitsky, V Grant, P Crystal nucleation in metallic alloys using x-ray radiography and machine learning |
| title | Crystal nucleation in metallic alloys using x-ray radiography and machine learning |
| title_full | Crystal nucleation in metallic alloys using x-ray radiography and machine learning |
| title_fullStr | Crystal nucleation in metallic alloys using x-ray radiography and machine learning |
| title_full_unstemmed | Crystal nucleation in metallic alloys using x-ray radiography and machine learning |
| title_short | Crystal nucleation in metallic alloys using x-ray radiography and machine learning |
| title_sort | crystal nucleation in metallic alloys using x ray radiography and machine learning |
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