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:

A novel online tool condition monitoring method for milling titanium alloy with consideration of tool wear law
Bo Qin, Yongqing Wang, Kuo Liu, et al.
Mechanical Systems and Signal Processing (2023) Vol. 199, pp. 110467-110467
Closed Access | Times Cited: 25

Showing 25 citing articles:

A new cutting tool filled with metallic lattice and design method for vibration suppression in milling
Yun Yang, Yang Yang, Hua-Chen Liu, et al.
Mechanical Systems and Signal Processing (2024) Vol. 212, pp. 111310-111310
Closed Access | Times Cited: 9

ACWGAN-GP for milling tool breakage monitoring with imbalanced data
Xuebing Li, Caixu Yue, Xianli Liu, et al.
Robotics and Computer-Integrated Manufacturing (2023) Vol. 85, pp. 102624-102624
Closed Access | Times Cited: 20

Toward practical tool wear prediction paradigm with optimized regressive Siamese neural network
Jian Duan, Jianqiang Liang, Xinjia Yu, et al.
Advanced Engineering Informatics (2023) Vol. 58, pp. 102200-102200
Closed Access | Times Cited: 17

Digital-twin-driven intelligent tracking error compensation of ultra-precision machining
Zhicheng Xu, Baolong Zhang, Dongfang Li, et al.
Mechanical Systems and Signal Processing (2024) Vol. 219, pp. 111630-111630
Closed Access | Times Cited: 5

Tool wear monitoring based on physics-informed Gaussian process regression
Mingjian Sun, Xianding Wang, Kai Guo, et al.
Journal of Manufacturing Systems (2024) Vol. 77, pp. 40-61
Closed Access | Times Cited: 5

Notifying Type-2 Error and Segregating Undefined Conditions in Health Monitoring of Milling Cutter: A Statistical and Deep Learning Approach
Aditya Sanju, Abhishek D. Patange, Aditya M. Rahalkar, et al.
Journal of Vibration Engineering & Technologies (2025) Vol. 13, Iss. 1
Closed Access

A task-cooperative drilling monitoring method based on heterogeneous multi-task learning: Task adaptive fusion guided by domain knowledge
Jing Qin, Qinghua Song, Runqiong Wang, et al.
Mechanical Systems and Signal Processing (2025) Vol. 225, pp. 112299-112299
Closed Access

Development of an anti-vibration cutting tool combining the lattice structures infill with damping particles
Yun Yang, Hao-Lin Liu, Jiawei Yuan, et al.
Mechanical Systems and Signal Processing (2025) Vol. 228, pp. 112425-112425
Closed Access

Intelligent wireless tool wear monitoring system based on chucked tool condition monitoring ring and deep learning
Ni Chen, Zhan Liu, Zhongling Xue, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103176-103176
Closed Access

Research on tap breakage monitoring method for tapping process based on SSAELSTM fusion network
Ting Chen, Jianming Zheng, Chao Peng, et al.
Measurement (2024) Vol. 236, pp. 115076-115076
Closed Access | Times Cited: 2

A trochoidal toolpath planning method for 5-axis milling of blisks with equal radial cutting depth
Xing Dai, Qi Qi, Jixiang Yang, et al.
Journal of Manufacturing Processes (2024) Vol. 123, pp. 128-141
Closed Access | Times Cited: 2

Recent Progress of Chatter Detection and Tool Wear Online Monitoring in Machining Process: A Review and Future Prospects
Feng-ze Qin, Huajun Cao, Guibao Tao, et al.
International Journal of Precision Engineering and Manufacturing-Green Technology (2024) Vol. 12, Iss. 2, pp. 719-748
Closed Access | Times Cited: 2

Tool wear classification based on maximal overlap discrete wavelet transform and hybrid deep learning model
Ahmed Abdeltawab, Xi Zhang, Zhang longjia
The International Journal of Advanced Manufacturing Technology (2023) Vol. 130, Iss. 5-6, pp. 2381-2406
Closed Access | Times Cited: 5

Research progress on intelligent monitoring of tool condition based on deep learning
Dahu Cao, Wei Liu, Jimin Ge, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 134, Iss. 5-6, pp. 2129-2150
Closed Access | Times Cited: 1

A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring
Haoyuan Zhang, Shanglei Jiang, Daqing Gao, et al.
Machines (2024) Vol. 12, Iss. 12, pp. 833-833
Open Access | Times Cited: 1

Interpretable tool wear monitoring: Architecture with large-scale CNN and adaptive EMD
Yi Sun, Hong‐Liang Song, Hongli Gao, et al.
Journal of Manufacturing Systems (2024) Vol. 78, pp. 294-307
Closed Access | Times Cited: 1

Milling tool condition monitoring for difficult-to-cut materials based on NCAE and IGWO-SVM
Siqi Wang, Shichao Yan, Yuwen Sun
The International Journal of Advanced Manufacturing Technology (2023) Vol. 129, Iss. 3-4, pp. 1355-1374
Closed Access | Times Cited: 2

Research on BO-CNN Based Tool Wear Status Monitoring Method
Shuo Wang, Zhenliang Yu, Jian Zhang, et al.
Mechanisms and machine science (2024), pp. 160-166
Closed Access

Mapping of Strategic Operating Conditions for End Milling Super-Transus Heat-Treated Ti1023 Alloy Using Multi-Objective Optimization
Viswajith S. Nair, K. Rameshkumar, V. Satyanarayana, et al.
Arabian Journal for Science and Engineering (2024)
Closed Access

A causal based method for denoising non-homologous noises in time series manufacturing monitoring data
Changqing Liu, Yingguang Li, Jiaqi Hua, et al.
Journal of Manufacturing Systems (2024) Vol. 76, pp. 92-102
Closed Access

Tool State Recognition Based on POGNN-GRU under Unbalanced Data
Weiming Tong, Jiaqi Shen, Zhongwei Li, et al.
Sensors (2024) Vol. 24, Iss. 16, pp. 5433-5433
Open Access

Siamese Neural Network and Multimodal Data Fusion Approach for Small-Sample Learning in Industrial Soft Sensor Modeling
Yuchen Zhao, Zhe Liu, Yan Feng, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 20, pp. 33763-33777
Closed Access

Page 1

Scroll to top