Journal
MATERIALS & DESIGN
Volume 198, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2020.109290
Keywords
Powder metallurgy; Bayesian optimization; Gas atomization; Ni-Co based superalloy; Turbine disk
Categories
Funding
- Cross-ministerial Strategic Innovation Promotion Program (SIP)
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In this study, machine learning-driven optimization was applied to determine promising gas atomization process parameters for the manufacture of Ni-Co based superalloy powders, leading to a substantial increase in yield and a significant reduction in manufacturing cost compared with commercial powders.
The process parameters in powder manufacturing must be optimized to produce high-quality powders with desired sizes depending on the use. Machine learning-driven optimization was applied to determine promising gas atomization process parameters for the manufacture of Ni-Co based superalloy powders for turbine-disk applications. Using a Bayesian optimization without expert assistance, starting from just three sets of data, three optimization cycles were used to determine the gas atomization process parameters. In particular, we determined the melt temperature and gas pressure that could achieve a 77.85% yield (size: <53 mu m), compared to the 10-30% yield that is generally achieved. This substantial increase in yield enabled us to successfully reduce the manufacturing cost by similar to 72% compared with that of a commercial powder. (C) 2020 The Authors. Published by Elsevier Ltd.
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