DOAJ Open Access 2021

Phase prediction of Ni-base superalloys via high-throughput experiments and machine learning

Zijun Qin Zi Wang Yunqiang Wang Lina Zhang Weifu Li +11 lainnya

Lihat Sumber DOI

Abstrak

Predicting the phase precipitation of multicomponent alloys, especially the Ni-base superalloys, is a difficult task. In this work, we introduced a dependable and efficient way to establish the relationship between composition and detrimental phases in Ni-base superalloys, by integrating high throughput experiments and machine learning algorithms. 8371 sets of data about composition and phase information were obtained rapidly, and analyzed by machine learning to establish a high-confidence phase prediction model. Compared with the traditional methods, the proposed approach has remarkable advantage in acquiring and analyzing the experimental data, which can also be applied to other multicomponent alloys. IMPACT STATEMENT By integrating the high throughput experiments and machine learning algorithms, it is hopeful to facilitate the design of new Ni-base superalloys, and even other multicomponent alloys.

Topik & Kata Kunci

Materials of engineering and construction. Mechanics of materials

Penulis (16)

Zijun Qin

Zi Wang

Yunqiang Wang

Lina Zhang

Weifu Li

Jin Liu

Zexin Wang

Zihang Li

Jun Pan

Lei Zhao

Feng Liu

Liming Tan

Jianxin Wang

Hua Han

Liang Jiang

Yong Liu

Format Sitasi

APA MLA BibTeX

Qin, Z., Wang, Z., Wang, Y., Zhang, L., Li, W., Liu, J. et al. (2021). Phase prediction of Ni-base superalloys via high-throughput experiments and machine learning. https://doi.org/10.1080/21663831.2020.1815093

Akses Cepat

Lihat di Sumber doi.org/10.1080/21663831.2020.1815093

Informasi Jurnal

Tahun Terbit: 2021
Sumber Database: DOAJ
DOI: 10.1080/21663831.2020.1815093
Akses: Open Access ✓