The learning of the precipitates morphological parameters from the composition of Nickel-based superalloys

Release time:2021-04-16

Impact Factor:6.289

Journal:Materials Design

Key Words:Machine learning, precipitated-phase characteristics, Ni-based Superalloy

Abstract:It becomes a common practice to adopt high-throughput experiments on superalloys, which can generate a large amount of data. To address this large amount of data, we designed a machine learning (ML) based model to automate the experimental analysis process. More specifically, we adopted the Unet algorithm to segment the precipitated phases from superalloy images and subsequently used a regression algorithm to predict the morphological parameters of the microstructure of the segmented precipitated phases according to their composition. The method proposed in this work may provide guidance for the future design of the superalloy composition.

Co-author:刘锦, Zi Wang, 鲁鸣鸣

First Author:王运强

Indexed by:Journal paper

Correspondence Author:王建新

Translation or Not:no

Date of Publication:2021-04-15

Included Journals:SCI