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:

Feature selection for predicting tool wear of machine tools
Wen-Nan Cheng, Chih-Chun Cheng, Yao-Hsuan Lei, et al.
The International Journal of Advanced Manufacturing Technology (2020) Vol. 111, Iss. 5-6, pp. 1483-1501
Closed Access | Times Cited: 19

Showing 19 citing articles:

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions
Sameer Sayyad, Satish Kumar, Arunkumar Bongale, et al.
IEEE Access (2021) Vol. 9, pp. 110255-110286
Open Access | Times Cited: 79
MS-SSPCANet: A powerful deep learning framework for tool wear prediction
Jian Duan, Cheng Hu, Xiaobin Zhan, et al.
Robotics and Computer-Integrated Manufacturing (2022) Vol. 78, pp. 102391-102391
Closed Access | Times Cited: 44
Robust Tool Wear Prediction using Multi-Sensor Fusion and Time-Domain Features for the Milling Process using Instance-based Domain Adaptation
Vivek Warke, Satish Kumar, Arunkumar Bongale, et al.
Knowledge-Based Systems (2024) Vol. 288, pp. 111454-111454
Closed Access | Times Cited: 10
Tool wear prediction using long short-term memory variants and hybrid feature selection techniques
Sameer Sayyad, Satish Kumar, Arunkumar Bongale, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 121, Iss. 9-10, pp. 6611-6633
Closed Access | Times Cited: 29
Bayesian-based uncertainty-aware tool-wear prediction model in end-milling process of titanium alloy
Gyeongho Kim, Sang Min Yang, Dong Min Kim, et al.
Applied Soft Computing (2023) Vol. 148, pp. 110922-110922
Open Access | Times Cited: 20
Intelligent milling tool wear estimation based on machine learning algorithms
Yunus Emre Karabacak
Journal of Mechanical Science and Technology (2024) Vol. 38, Iss. 2, pp. 835-850
Closed Access | Times Cited: 7
Deep learning-based anomaly-onset aware remaining useful life estimation of bearings
Pooja Kamat, Rekha Sugandhi, Satish Kumar
PeerJ Computer Science (2021) Vol. 7, pp. e795-e795
Open Access | Times Cited: 34
Improving the useful life of tools using active vibration control through data-driven approaches: A systematic literature review
Vivek Warke, Satish Kumar, Arunkumar Bongale, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 128, pp. 107367-107367
Open Access | Times Cited: 13
AI-Driven Wear Monitoring of PVD TiAlN Coated Carbide Insert in Sustainable Machining of Hastelloy C276: An Industry 4.0 Perspective
Binayak Sen, Subhankar Saha, Raman Kumar, et al.
Results in Engineering (2025), pp. 104457-104457
Open Access
A data and knowledge-driven cutting parameter adaptive optimization method considering dynamic tool wear
Congbo Li, Xikun Zhao, Huajun Cao, et al.
Robotics and Computer-Integrated Manufacturing (2022) Vol. 81, pp. 102491-102491
Closed Access | Times Cited: 21
Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition
Houssem Habbouche, Tarak Benkedjouh, Noureddine Zerhouni
The International Journal of Advanced Manufacturing Technology (2021) Vol. 114, Iss. 1-2, pp. 145-157
Closed Access | Times Cited: 24
Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models
Satish Kumar, Tushar Kolekar, Ketan Kotecha, et al.
International Journal of Quality & Reliability Management (2022) Vol. 39, Iss. 7, pp. 1551-1576
Closed Access | Times Cited: 18
Robust tool wear monitoring system development by sensors and feature fusion
Yu‐Ru Lin, Ching‐Hung Lee, Ming‐Chyuan Lu
Asian Journal of Control (2022) Vol. 24, Iss. 3, pp. 1005-1021
Closed Access | Times Cited: 13
Correlation Analysis of Vibration Signal Frequency with Tool Wear During the Milling Process on Martensitic Stainless Steel Material
Achmad Zaki Rahman, Khairul Jauhari, Mahfudz Al Huda, et al.
Arabian Journal for Science and Engineering (2023) Vol. 49, Iss. 8, pp. 10573-10586
Closed Access | Times Cited: 7
ANFIS-based Framework for the Prediction of Bearing’s Remaining Useful Life
Abdel wahhab Lourari, Tarak Benkedjouh, Bilal El Yousfi, et al.
International Journal of Prognostics and Health Management (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 1
A new embedded vision system for monitoring tool conditions in production lines using a combination of direct and indirect methods
Henry Peterson Scharf, Heraldo Nelio Cambraia, Dalberto Dias da Costa
Journal of Manufacturing Processes (2023) Vol. 102, pp. 143-153
Closed Access | Times Cited: 4
Signal processing and machine learning as a tool for identifying idling noises of different circular saw blades
Mira Miric-Milosavljević, Srdjan Svrzić, Zoran Nikolić, et al.
BioResources (2024) Vol. 19, Iss. 1, pp. 1744-1756
Open Access | Times Cited: 1
Comparative evaluation of feature selection methods and deep learning models for precise tool wear prediction
Anuj Kumar, V. Vasu
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access
Health Indicator Construction for Milling Tool Wear Monitoring With Multi-sensor Fusion
Shuhao Zhang, Bin Zhang, Congying Deng
2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS) (2023), pp. 1-5
Closed Access
Page 1

Scroll to top