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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime
Yinfeng Jia, Rui Fu, Chao Ling, et al.
International Journal of Fatigue (2023) Vol. 172, pp. 107645-107645
Closed Access | Times Cited: 28

Showing 1-25 of 28 citing articles:

A review of the multi-dimensional application of machine learning to improve the integrated intelligence of laser powder bed fusion
Kun Li, Ruijin Ma, Qin Yu, et al.
Journal of Materials Processing Technology (2023) Vol. 318, pp. 118032-118032
Closed Access | Times Cited: 36
Recent advances in machine learning-assisted fatigue life prediction of additive manufactured metallic materials: A review
Hao Wang, Shanglin Gao, Boyi Wang, et al.
Journal of Material Science and Technology (2024) Vol. 198, pp. 111-136
Closed Access | Times Cited: 20
High cycle fatigue life prediction of titanium alloys based on a novel deep learning approach
Siyao Zhu, Yue Zhang, Beichen Zhu, et al.
International Journal of Fatigue (2024) Vol. 182, pp. 108206-108206
Closed Access | Times Cited: 12
Machine learning for predicting fatigue properties of additively manufactured materials
Min Yi, Ming Xue, Peihong Cong, et al.
Chinese Journal of Aeronautics (2023) Vol. 37, Iss. 4, pp. 1-22
Open Access | Times Cited: 21
High-temperature high-cycle fatigue performance and machine learning-based fatigue life prediction of additively manufactured Hastelloy X
Liming Lei, Bo Li, Haijie Wang, et al.
International Journal of Fatigue (2023) Vol. 178, pp. 108012-108012
Closed Access | Times Cited: 19
Crack propagation simulation and overload fatigue life prediction via enhanced physics-informed neural networks
Zhiying Chen, Yanwei Dai, Yinghua Liu
International Journal of Fatigue (2024) Vol. 186, pp. 108382-108382
Closed Access | Times Cited: 7
Continuum damage mechanics-based fatigue life prediction of L-PBF Ti-6Al-4V
Rui Fu, Chao Ling, Liang Zheng, et al.
International Journal of Mechanical Sciences (2024) Vol. 273, pp. 109233-109233
Closed Access | Times Cited: 6
Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling
Zhixin Zhan, Xiaofan He, Dingcheng Tang, et al.
Fatigue & Fracture of Engineering Materials & Structures (2023) Vol. 46, Iss. 12, pp. 4425-4464
Closed Access | Times Cited: 18
Fatigue life prediction driven by mesoscopic defect data
Chao Wang, Yali Yang, Hao Chen, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 131, pp. 107773-107773
Closed Access | Times Cited: 4
Role of Machine Learning in Additive Manufacturing of Titanium Alloys—A Review
Uma Maheshwera Reddy Paturi, Sai Teja Palakurthy, Suryapavan Cheruku, et al.
Archives of Computational Methods in Engineering (2023) Vol. 30, Iss. 8, pp. 5053-5069
Closed Access | Times Cited: 9
Corrosion fatigue life prediction method of aluminum alloys based on back-propagation neural network optimized by Improved Grey Wolf optimization algorithm
GaoFei Ji, Zhipeng Li, LingHui Hu, et al.
Journal of Materials Science (2024) Vol. 59, Iss. 23, pp. 10309-10323
Closed Access | Times Cited: 2
Multi-physics information-integrated neural network for fatigue life prediction of additively manufactured Hastelloy X superalloy
Haijie Wang, Bo Li, Liming Lei, et al.
Virtual and Physical Prototyping (2024) Vol. 19, Iss. 1
Open Access | Times Cited: 2
Predicting daily solar radiation using a novel hybrid long short-term memory network across four climate regions of China
Liwen Xing, Ningbo Cui, Li Guo, et al.
Computers and Electronics in Agriculture (2023) Vol. 212, pp. 108139-108139
Open Access | Times Cited: 7
Mechanical Properties of Ti Grade 2 Manufactured Using Laser Beam Powder Bed Fusion (PBF-LB) with Checkerboard Laser Scanning and In Situ Oxygen Strengthening
Bartłomiej Wysocki, Agnieszka Chmielewska‐Wysocka, P. Maj, et al.
Crystals (2024) Vol. 14, Iss. 6, pp. 574-574
Open Access | Times Cited: 1
A physics‐informed neural network framework based on fatigue indicator parameters for very high cycle fatigue life prediction of an additively manufactured titanium alloy
Hang Li, Guanze Sun, Tian Zhao, et al.
Fatigue & Fracture of Engineering Materials & Structures (2024)
Closed Access | Times Cited: 1
Parameter automatic optimization strategy for laser powder bed fusion using neural network infrared radiation intensity prediction model
Yanbing Liu, Jikang Li, Cheng Tan, et al.
Additive manufacturing (2024) Vol. 92, pp. 104373-104373
Closed Access | Times Cited: 1
Nondestructive Fatigue Life Prediction for Additively Manufactured Metal Parts through a Multimodal Transfer Learning Framework
Anyi Li, Arun Poudel, Shuai Shao, et al.
IISE Transactions (2024), pp. 1-16
Closed Access | Times Cited: 1
The fatigue mechanism and a new defect-based life prediction model for selective laser melted Al-Mg-Sc-Zr alloy
Jun Zou, Xiaoyu Xia, Zhenyu Feng, et al.
International Journal of Fatigue (2024) Vol. 190, pp. 108590-108590
Closed Access | Times Cited: 1
A frequency domain enhanced multi-view neural network approach to multiaxial fatigue life prediction for various metal materials
Shuonan Chen, Xuhong Zhou, Yongtao Bai
International Journal of Fatigue (2024), pp. 108620-108620
Closed Access | Times Cited: 1
Fatigue properties of binary Ti-Ta metal-metal composite with lamellar microstructure
Shenghang Xu, Meng Han, Kaijie Shen, et al.
Journal of Central South University (2023) Vol. 30, Iss. 9, pp. 2878-2889
Closed Access | Times Cited: 3
Crack Propagation Simulation and Overload Fatigue Life Prediction Via Enhanced Physics-Informed Neural Networks
Zhiying Chen, Yanwei Dai, Yinghua Liu
(2024)
Closed Access
Machine Learning-Based Prediction of Fatigue Strength in Additively Manufactured Ti-6al-4v Parts: A Sensitivity Analysis of Input Features
Michael Andrew Hills, Thorsten Hermann Becker
(2024)
Closed Access
Multi-feature parallel prediction for the enhanced sparse data in laser processing based on an improved reinforced machine learning method
Chao Liu, Juanjuan Zheng, Sanyang Liu, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102762-102762
Closed Access
High-Cycle Fatigue Performance of Laser Powder Bed Fusion Ti-6Al-4V Alloy with Inherent Internal Defects: A Critical Literature Review
Zongchen Li, Christian Affolter
Metals (2024) Vol. 14, Iss. 9, pp. 972-972
Open Access
A deep learning dataset for metal multiaxial fatigue life prediction
Shuonan Chen, Yongtao Bai, Xuhong Zhou, et al.
Scientific Data (2024) Vol. 11, Iss. 1
Open Access
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