Remaining fatigue life prediction of additively manufactured Inconel 718 alloy based on in-situ SEM and deep learning-Reference-Cited by-同舟云学术

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Remaining fatigue life prediction of additively manufactured Inconel 718 alloy based on in-situ SEM and deep learning

Published:2024-09 Issue: Volume:163 Page:108440
ISSN:1350-6307
Container-title:Engineering Failure Analysis
language:en
Short-container-title:Engineering Failure Analysis

Author:

Zhang Yixu,Wang Ni,Zhou Jianli,Wang Hao,Tang Liang,Zhang Yuefei,Zhang Ze

Funder

Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering

National Major Science and Technology Projects of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference29 articles.

1. Gaussian process regression based remaining fatigue life prediction for metallic materials under two-step loading;Gao;Int. J. Fatigue,2022

2. A Review on Fatigue Life Prediction Methods for Metals;Santecchia;Adv. Mater. Sci. Eng.,2016

3. Fatigue life prediction of notched components under size effect using stress gradient-based approach;Ye;Int. J. Fract.,2021

4. Estimation of remaining fatigue life under two-step loading based on kernel-extreme learning machine;Gan;Int. J. Fatigue,2021

5. Predicting the total fatigue life in metals;Makkonen;Int. J. Fatigue,2009

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Detecting APS failures using LSTM-AE and anomaly transformer enhanced with human expert analysis;Engineering Failure Analysis;2024-11

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