Very High Cycle Fatigue Life Prediction of SLM AlSi10Mg Based on CDM and SVR Models-Reference-Cited by-同舟云学术

Very High Cycle Fatigue Life Prediction of SLM AlSi10Mg Based on CDM and SVR Models

Published:2023-09-21 Issue:9 Volume:10 Page:823
ISSN:2226-4310
Container-title:Aerospace
language:en
Short-container-title:Aerospace

Author:

Yu Yibing¹,Sun Linlin²³,Bian Zhi⁴⁵,Wang Xiaojia⁴⁵^ORCID,Zhang Zhe⁴⁵,Song Chao⁴⁵,Hu Weiping¹,Chen Xiao⁶

Affiliation:

1. National Key Laboratory of Strength and Structural Integrity, School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China

2. Railway Engineering Research Institute, China Academy of Railway Sciences Corporation Ltd., Beijing 100081, China

3. State Key Laboratory of High-Speed Railway Track System, Beijing 100081, China

4. AVIC China Aero-Polytechnology Establishment, Beijing 100028, China

5. Laboratory of Quality Infrastructure Efficacy Research, General Administration of Quality Supervision, Inspection and Quarantine, Beijing 100028, China

6. Department of Engineering Mechanics, Institute of Biomechanics and Medical Engineering, Tsinghua University, Beijing 100084, China

Abstract

A novel fatigue evolution model considering the effect of defect size and additive manufacturing building direction based on the theories of continuum damage mechanics and its numerical implementation in ABAQUS is proposed in this paper. First, the constitutive model, fatigue damage evolution model and their parameter calibration methods are presented. Second, using the ABAQUS platform, the proposed model is implemented with user-defined subroutines. After that, based on the proposed model and its numerical implementation, the fatigue life of additively manufactured AlSi10Mg is predicted and its applicability is verified through experimental results. Finally, a support vector regression model is established to predict the fatigue life, and its results are compared to those of the numerical finite element method. The results show that the support vector regression model makes better predictions than the finite element method.

Funder

scientific research project of China Academy of Railway Sciences Co., Ltd.

Natural Science Foundation of China

Publisher

MDPI AG

Subject

Aerospace Engineering

Link

https://www.mdpi.com/2226-4310/10/9/823/pdf

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