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| Abstract No.: |
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Scheduled at:
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Thursday, May 05, 2022, Hall G2 4:20 PM
High Entropy Alloys I
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| Title: |
Design of high entropy alloys for thermal spray using machine learning
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| Authors: |
Spyros Kamnis* / Monitor Coatings-Castolin Eutectic, Great Britain
Sergio Gonzales / Northumbria University, UK
Athanasios Sfikas/ Northumbria University, UK
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| Abstract: |
High Entropy Alloys (HEAs) are a rapidly expanding opportunity with cross-cutting applicability, serving as both a platform for economic growth and an essential technology to realize key priorities from health to energy efficiency and pressing societal challenges. Wide-spread utilization of HEAs, and a return on these investments, critically rely on advancing HEAs from lab-scale prototypes and simulations to products produced at scale. Thermal spray techniques offer great scalability and should be at the forefront of these developments. In this study we first present a methodology for material design that is based on directional plots of a fitted machine learning model. Two HEAs are down selected and manufactured using vacuum arc melting, analysed and compared with the equivalent gas atomised powder. Finally, the powders are used to produce coatings via a High Velocity Oxy Air Fuel process followed by advanced characterisation. The results show that the deposits retain the properties of the feedstock material and under specific spray conditions the coatings exhibit even better properties compared to the as cast HEAs.
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