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

Yunqiang Wang; Mingming Lu; Zi Wang; Jin Liu; Lei Xu; Zijun Qin; Zexin Wang; Bingfeng Wang; Feng Liu; Jianxin Wang

Materials & Design (Aug 2021)

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

Yunqiang Wang,
Mingming Lu,
Zi Wang,
Jin Liu,
Lei Xu,
Zijun Qin,
Zexin Wang,
Bingfeng Wang,
Feng Liu,
Jianxin Wang

Affiliations

Yunqiang Wang: School of Computer Science and Engineering, Central South University, Changsha, People’s Republic of China
Mingming Lu: School of Computer Science and Engineering, Central South University, Changsha, People’s Republic of China
Zi Wang: AECC Commercial Aircraft Engine Co., Ltd., Shanghai, People’s Republic of China
Jin Liu: School of Computer Science and Engineering, Central South University, Changsha, People’s Republic of China
Lei Xu: School of Computer Science and Engineering, Central South University, Changsha, People’s Republic of China
Zijun Qin: The State Key Laboratory of Powder Metallurgy, Central South University, Changsha, People’s Republic of China
Zexin Wang: The State Key Laboratory of Powder Metallurgy, Central South University, Changsha, People’s Republic of China
Bingfeng Wang: School of Materials Science and Engineering, Central South University, Changsha, People’s Republic of China; Corresponding authors at: School of Computer Science and Engineering, Central South University, Changsha 410083, People’s Republic of China (J. Wang), The State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, People’s Republic of China (F. Liu). School of Materials Science and Engineering, Central South University, Changsha 410083, People’s Republic of China (B. Wang).
Feng Liu: The State Key Laboratory of Powder Metallurgy, Central South University, Changsha, People’s Republic of China; Corresponding authors at: School of Computer Science and Engineering, Central South University, Changsha 410083, People’s Republic of China (J. Wang), The State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, People’s Republic of China (F. Liu). School of Materials Science and Engineering, Central South University, Changsha 410083, People’s Republic of China (B. Wang).
Jianxin Wang: School of Computer Science and Engineering, Central South University, Changsha, People’s Republic of China; Corresponding authors at: School of Computer Science and Engineering, Central South University, Changsha 410083, People’s Republic of China (J. Wang), The State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, People’s Republic of China (F. Liu). School of Materials Science and Engineering, Central South University, Changsha 410083, People’s Republic of China (B. Wang).

Journal volume & issue: Vol. 206
p. 109747

Abstract

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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.

Published in Materials & Design

ISSN: 0264-1275 (Print); 1873-4197 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
Website: https://www.journals.elsevier.com/materials-and-design

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