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7175 |
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Tuesday, June 21, 2022, Saal Brüssel 3:00 PM Advanced filler alloys |
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The performance of a brazing filler based on a eutectic high entropy alloy designed by machine learning |
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Xavier Sanuy Morell* / University of Sheffield, Great Britain Russell Goodall / The University of Sheffield, United Kingdom Ed Pickering/ The University of Manchester, United Kingdom Pat Rodgers/ VBC Group, United Kingdom Phil Webb/ VBC Group, United Kingdom |
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Future applications for brazing will demand new filler metals that perform at higher temperatures, improve on the current mechanical properties, have better oxidation resistance and have the capability to easily braze materials with different chemical natures (e.g., metal to ceramics). As a result of this complex mix of requirements, new alloys such as Eutectic High Entropy Alloys (EHEAs), have been sought as a possible solution. That is because of their good behaviour at high temperatures, as has been reported for some High Entropy Alloys (HEAs), but also as they can be expected to show lower melting points than might commonly be found for combinations of refractory elements. Moreover, their isothermal solidification behaviour implies no segregation, as well as the dual-phase microstructure could provide high Young’s modulus and ductility at the same time. However, the design of new EHEAs represents a challenge, due to two factors; the millions of possible combinations of elements and stoichiometry that can be formed, as well as the lack of reliable design tools to help defining the eutectic composition. Machine learning has shown promising results distinguishing between HEAs and EHEAs, by the use of thermodynamic, electronic and atomic size features. This has allowed us to make and test new EHEAs, and the presentation will cover the initial characterization and performance of those novel alloys designed by machine learning algorithms and used as active filler at high temperature to braze metal-ceramic materials. |