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An Entropy-Based Neighborhood Rough Set and PSO-SVRM Model for Fatigue Life Prediction of Titanium Alloy Welded Joints

Authors
  • Zou, Li1, 2, 3
  • Sun, Yibo2
  • Yang, Xinhua2
  • 1 Software Technology Institute, Dalian Jiaotong University, Dalian 116028, China
  • 2 Dalian Key Laboratory of Welded Structures and Its Intelligent Manufacturing Technology (IMT) of Rail Transportation Equipment, Dalian Jiaotong University, Dalian 116028, China
  • 3 Sichuan Provincial Key Lab of Process Equipment and Control, Zigong 643000, China
Type
Published Article
Journal
Entropy
Publisher
MDPI AG
Publication Date
Jan 27, 2019
Volume
21
Issue
2
Identifiers
DOI: 10.3390/e21020117
PMID: 33266833
PMCID: PMC7514600
Source
PubMed Central
Keywords
Disciplines
  • Article
License
Green

Abstract

In order to obtain comprehensive assessment of the factors influencing fatigue life and to further improve the accuracy of fatigue life prediction of welded joints, soft computing methods, including entropy-based neighborhood rough set reduction algorithm, the particle swarm optimization (PSO) algorithm and support vector regression machine (SVRM) are combined to construct a fatigue life prediction model of titanium alloy welded joints. By using an entropy-based neighborhood rough set reduction algorithm, the influencing factors of the fatigue life of titanium alloy welded joints such as joint type, plate thickness, etc. are analyzed and the reduction results are obtained. Fatigue characteristic domains are proposed and determined subsequently according to the reduction results. The PSO-SVRM model for fatigue life prediction of titanium alloy welded joints is established in the suggested fatigue characteristic domains. Experimental results show that by taking into account the impact of joint type, the PSO-SVRM model could better predict the fatigue life of titanium alloy welded joints. The PSO-SVRM model indicates the relationship between fatigue life and fatigue life influencing factors in multidimensional space compared with the conventional least-square S-N curve fitting method, it could predict the fatigue life of the titanium alloy welded joints more accurately thus helps to the reliability design of the structure.

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