The interpretable descriptors for fatigue performance of wrought aluminum alloys

Aluminum alloys are extensively utilized in the transportation and aerospace industries due to their excellent processability and corrosion resistance. A key aspect affecting the application of aluminum alloys is fatigue failure, with fatigue strength constituting an essential indicator for assessing the failure mode. In this study, 859 data sets were collected to evaluate the effects of aluminum alloy composition and mechanical properties on fatigue strength. To comprehensively evaluate the features that exert a great influence on the fatigue strength of wrought aluminum alloys, atomic physical quantities of aluminum alloys were added to the original data set. To improve the computational efficiency and physical interpretability of the model, Spearman correlation analysis, optimal subset method, and other methods were used to perform feature selection on the atomic feature quantities and mechanical properties in the data set. Finally, a mathematical relationship between material descriptors and fatigue strength was established based on the alloy composition and the three most relevant features in the feature screening, which helps to better understand and predict the fatigue behavior of wrought aluminum alloys.

Authors

Institutions

Publication Details

Journal
Journal of Materials Research and Technology
Published
2024-08-26
DOI
https://doi.org/10.1016/j.jmrt.2024.08.160
Citations
15
Primary Topic
Aluminum Alloy Microstructure Properties
Type
article
Field-Weighted Citation Impact
19.48

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The interpretable descriptors for fatigue performance of wrought aluminum alloys

Hong‐Hui Wu, Guilin Wu, Junheng Gao, Fengfan Li et al.
15 citations
Journal of Materials Research and Technology
Aluminum Alloy Microstructure Properties
19.48
article

The interpretable descriptors for fatigue performance of wrought aluminum alloys

Hong‐Hui Wu, Guilin Wu, Junheng Gao, Fengfan Li, Chunlei Shang, Shuize Wang, Dexin Zhu, Chaolei Zhang, Yufan Liu, Penghui Bai, Haitao Zhao, Jiahao Ji, Jianlong Kuang
article en
15 citations

Abstract

Aluminum alloys are extensively utilized in the transportation and aerospace industries due to their excellent processability and corrosion resistance. A key aspect affecting the application of aluminum alloys is fatigue failure, with fatigue strength constituting an essential indicator for assessing the failure mode. In this study, 859 data sets were collected to evaluate the effects of aluminum alloy composition and mechanical properties on fatigue strength. To comprehensively evaluate the features that exert a great influence on the fatigue strength of wrought aluminum alloys, atomic physical quantities of aluminum alloys were added to the original data set. To improve the computational efficiency and physical interpretability of the model, Spearman correlation analysis, optimal subset method, and other methods were used to perform feature selection on the atomic feature quantities and mechanical properties in the data set. Finally, a mathematical relationship between material descriptors and fatigue strength was established based on the alloy composition and the three most relevant features in the feature screening, which helps to better understand and predict the fatigue behavior of wrought aluminum alloys.

Journal of Materials Research and TechnologyVol. 32
Liaoning Academy of Materials, University of Science and Technology Beijing (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 1%
Aluminum Alloy Microstructure Properties
19.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.