Quantifying Solid Solution Strengthening in Nickel-Based Superalloys via High-Throughput Experiment and Machine Learning
发布时间：2023-03-08
点击次数：
影响因子：2.4
DOI码：10.32604/cmes.2022.021639
所属单位：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
教研室：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
发表刊物：Computer Modeling in Engineering & Sciences
项目来源：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）
关键字：Multicomponent diffusion multiples, Solid solution strengthening, strengthening models, Machine learning
摘要：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.
合写作者：Zexin Wang, Zi Wang, Zijun Qin, Feng Liu, Xueling Fan, Lan Huang
第一作者：Zihang Li
论文类型：Article
通讯作者：谭黎明, Xiaochao Jin
学科门类：工学
一级学科：冶金
卷号：135
期号：2
页面范围：1521-1538
ISSN号：1526-1492
是否译文：否
发表时间：2023-03-08