Impact Factor:2.4
DOI number:10.32604/cmes.2022.021639
Affiliation of Author(s):中南大学粉末冶金研究院，中国上海AECC 商用航空发动机有限公司，西安航空工程学院
Teaching and Research Group:高温结构材料研究所，强度与振动力学结构国家重点实验室
Journal:Computer Modeling in Engineering & Sciences
Funded by:国家科学技术大项目（J2019-IV-0003-0070）、中国自然科学基金（91860105、52074366）、中国博士后科学基金会（2019M662799）、中国湖南省自然科学基金（2021JJ40757）、湖南省科技创新计划（2021RC3131）、长沙市自然科学基金（kq2014126）
Key Words:Multicomponent diffusion multiples, Solid solution strengthening, strengthening models, Machine learning
Abstract:Solid solution strengthening (SSS) is one of the main contributions to the desired tensile properties of nickel-based superalloys for turbine blades and disks. The value of SSS can be calculated by using Fleischer's and Labusch's theories, while the model parameters are incorporated without fitting to experimental data of complex alloys. In this work, four diffusion multiples consisting of multicomponent alloys and pure Ni are prepared and characterized. The composition and microhardness of single gamma phase regions in samples are used to quantify the SSS. Then, Fleischer's and Labusch's theories are examined based on high-throughput experiments, respectively. The fitted solid solution coefficients are obtained based on Labusch's theory and experimental data, indicating higher accuracy. Furthermore, six machine learning algorithms are established, providing a more accurate prediction compared with traditional physical models and fitted physical models. The results show that the coupling of high-throughput experiments and machine learning has great potential in the field of performance prediction and alloy design.
Co-author:Zexin Wang, Zi Wang, Zijun Qin, Feng Liu, Xueling Fan, Lan Huang
First Author:Zihang Li
Indexed by:Article
Correspondence Author:Liming Tan, Xiaochao Jin
Discipline:Engineering
First-Level Discipline:冶金
Volume:135
Issue:2
Page Number:1521-1538
ISSN No.:1526-1492
Translation or Not:no