Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods
Haijie Wang, Bo Li, Jian‐Guo Gong, et al.
Engineering Fracture Mechanics (2023) Vol. 284, pp. 109242-109242
Closed Access | Times Cited: 104

Showing 1-25 of 104 citing articles:

A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities
Huiqin Li, Zhengxin Zhang, Tianmei Li, et al.
Mechanical Systems and Signal Processing (2024) Vol. 209, pp. 111120-111120
Closed Access | Times Cited: 65
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: 15
A fatigue life prediction framework of laser-directed energy deposition Ti-6Al-4V based on physics-informed neural network
Linwei Dang, Xiaofan He, Dingcheng Tang, et al.
International Journal of Structural Integrity (2025)
Closed Access | Times Cited: 1
Urban Transportation Planning
Sitesh Kumar Singh, Bader Eddin Alasali, Yuvraj Belbase
Advances in chemical and materials engineering book series (2025), pp. 469-504
Closed Access | Times Cited: 1
Physics-informed machine learning and its structural integrity applications: state of the art
Shun‐Peng Zhu, Lanyi Wang, Changqi Luo, et al.
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2023) Vol. 381, Iss. 2260
Closed Access | Times Cited: 28
Data-driven approach to very high cycle fatigue life prediction
Yu-Ke Liu, Jia-Le Fan, Gang Zhu, et al.
Engineering Fracture Mechanics (2023) Vol. 292, pp. 109630-109630
Closed Access | Times Cited: 23
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: 23
Evaluating fatigue onset in metallic materials: Problem, current focus and future perspectives
Enrico Salvati
International Journal of Fatigue (2024) Vol. 188, pp. 108487-108487
Open Access | Times Cited: 13
Machine Learning-Based predictions of crack growth rates in an aeronautical aluminum alloy
Yuval Freed
Theoretical and Applied Fracture Mechanics (2024) Vol. 130, pp. 104278-104278
Closed Access | Times Cited: 12
Low-cycle fatigue life prediction of austenitic stainless steel alloys: A data-driven approach with identification of key features
Harsh Kumar Bhardwaj, Mukul Shukla
International Journal of Fatigue (2024) Vol. 187, pp. 108454-108454
Open Access | Times Cited: 9
A tensile properties-related fatigue strength predicted machine learning framework for alloys used in aerospace
Jiangbo Fan, Zhangwei Wang, Changqi Liu, et al.
Engineering Fracture Mechanics (2024) Vol. 301, pp. 110057-110057
Closed Access | Times Cited: 6
A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders
Jorge Magalhães, Tomás Jorge, Rúben Silva, et al.
Railway Engineering Science (2024) Vol. 32, Iss. 4, pp. 421-443
Open Access | Times Cited: 6
Critical physics informed fatigue life prediction of laser 3D printed AlSi10Mg alloys with mass internal defects
Yanan Hu, Yufeng She, Shengchuan Wu, et al.
International Journal of Mechanical Sciences (2024), pp. 109730-109730
Closed Access | Times Cited: 6
A digital twin approach toward unlocking elasto-plastic fatigue crack growth rates under negative load ratios
Evan Wei Wen Cheok, Xudong Qian, Ser Tong Quek, et al.
Engineering Fracture Mechanics (2024) Vol. 301, pp. 110034-110034
Open Access | Times Cited: 5
A multi-scale experimental investigation on fatigue crack propagation rate behavior of powder bed fusion-laser beam 316L stainless steel subjected to various heat treatments
Wen Zhu, Ziad Moumni, Jihong Zhu, et al.
Engineering Fracture Mechanics (2024) Vol. 302, pp. 110064-110064
Closed Access | Times Cited: 5
Study of fatigue crack propagation on modified CT specimens under variable amplitude loadings using machine learning
B. Santos, V. Infante, T. Barros, et al.
International Journal of Fatigue (2024) Vol. 184, pp. 108332-108332
Open Access | Times Cited: 5
Machine learning-driven power prediction in continuous extrusion of pure titanium for enhanced structural resilience under extreme loading
Ahmed Ghazi Abdulameer, Muhannad M. Mrah, Maryam Bazerkan, et al.
Discover Materials (2025) Vol. 5, Iss. 1
Open Access
Study of Hybrid Machine Learning Multiaxial Low‐Cycle Fatigue Life Prediction Model of CP‐Ti
Tian‐Hao Ma, Wei Zhang, Le Chang, et al.
Fatigue & Fracture of Engineering Materials & Structures (2025)
Closed Access
High-cycle and very-high-cycle fatigue life prediction in additive manufacturing using hybrid physics-informed neural networks
Isaac Abiria, Chan Wang, Qicheng Zhang, et al.
Engineering Fracture Mechanics (2025), pp. 111026-111026
Closed Access
Multi-fidelity physics-informed machine learning framework for fatigue life prediction of additive manufactured materials
Lanyi Wang, Shun‐Peng Zhu, Borui Wu, et al.
Computer Methods in Applied Mechanics and Engineering (2025) Vol. 439, pp. 117924-117924
Closed Access
A multi-algorithm integration machine learning approach for high cycle fatigue prediction of a titanium alloy in aero-engine
Siyao Zhu, Yue Zhang, Xin Chen, et al.
Engineering Fracture Mechanics (2023) Vol. 289, pp. 109485-109485
Closed Access | Times Cited: 15
Uncertainty-aware fatigue-life prediction of additively manufactured Hastelloy X superalloy using a physics-informed probabilistic neural network
Haijie Wang, Bo Li, Liming Lei, et al.
Reliability Engineering & System Safety (2023) Vol. 243, pp. 109852-109852
Closed Access | Times Cited: 12
A physics-informed auto-encoder based cable force identification framework for long-span bridges
Xinyu Guo, Sheng-En Fang
Structures (2024) Vol. 60, pp. 105906-105906
Closed Access | Times Cited: 4
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: 4
Physics-informed machine learning for loading history dependent fatigue delamination of composite laminates
Liaojun Yao, Jiexiong Wang, Mingyue Chuai, et al.
Composites Part A Applied Science and Manufacturing (2024), pp. 108474-108474
Closed Access | Times Cited: 4
Page 1 - Next Page

Scroll to top