Design of Ni-based turbine disc superalloys with improved yield strength using machine learning
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Title
Design of Ni-based turbine disc superalloys with improved yield strength using machine learning
Authors
Keywords
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Journal
JOURNAL OF MATERIALS SCIENCE
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-06-02
DOI
10.1007/s10853-022-07295-5
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- (2018) Jarosław M. Granda et al. NATURE
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- A modelling approach to yield strength optimisation in a nickel-base superalloy
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- Microstructural investigation of thermally aged nickel-based superalloy 718Plus
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- Ranking the Stars: A Refined Pareto Approach to Computational Materials Design
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- Detecting Novel Associations in Large Data Sets
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- Strengthening Mechanisms in Polycrystalline Multimodal Nickel-Base Superalloys
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