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:

A hybrid-driven probabilistic state space model for tool wear monitoring
Zhipeng Ma, Ming Zhao, Xuebin Dai, et al.
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110599-110599
Closed Access | Times Cited: 19

Showing 19 citing articles:

Tool wear and remaining useful life estimation in precision machining using interacting multiple model
Qian Yang, Debasish Mishra, Utsav Awasthi, et al.
Journal of Manufacturing Systems (2024) Vol. 74, pp. 367-386
Closed Access | Times Cited: 8
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
Research on multi-step ahead prediction method for tool wear based on MSTCN-SBiGRU-MHA
Jing Xue, Yaonan Cheng, Wenjie Zhai, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103219-103219
Closed Access
A tool wear condition monitoring method for non-specific sensing signals
Yezhen Peng, Qinghua Song, Runqiong Wang, et al.
International Journal of Mechanical Sciences (2023) Vol. 263, pp. 108769-108769
Closed Access | Times Cited: 13
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
An uncertainty quantification and accuracy enhancement method for deep regression prediction scenarios
Teng Zhang, Fangyu Peng, Rong Yan, et al.
Mechanical Systems and Signal Processing (2025) Vol. 227, pp. 112394-112394
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
A novel algorithm for tool wear monitoring utilizing model and Knowledge-Guided Multi-Expert weighted adversarial deep transfer learning
Zhilie Gao, Ni Chen, Liang Li
Mechanical Systems and Signal Processing (2025) Vol. 228, pp. 112456-112456
Closed Access
An innovative Multisource Lightweight Adaptive Replayed Online Deep Transfer Learning algorithm for tool wear monitoring
Zhilie Gao, Ni Chen, Yinfei Yang, et al.
Journal of Manufacturing Processes (2024) Vol. 124, pp. 261-281
Closed Access | Times Cited: 2
An innovative multisource multibranch metric ensemble deep transfer learning algorithm for tool wear monitoring
Zhilie Gao, Ni Chen, Yingfei Yang, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102659-102659
Closed Access | Times Cited: 2
Research on multi-source information fusion tool wear monitoring based on MKW-GPR model
Ruitao Peng, Zelin Xiao, Yuanyuan Peng, et al.
Measurement (2024), pp. 116055-116055
Closed Access | Times Cited: 2
A dual knowledge embedded hybrid model based on augmented data and improved loss function for tool wear monitoring
Xuzhou Fang, Qinghua Song, Jing Qin, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 92, pp. 102901-102901
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
Research on tool wear prediction based on the random forest optimized by NGO algorithm
Yaonan Cheng, Shilong Zhou, Jing Xue, et al.
Machining Science and Technology (2024), pp. 1-24
Closed Access | Times Cited: 1
Application of physics-guided deep learning model in tool wear monitoring of high-speed milling
Shenshen Li, Jun Li, Kunpeng Zhu
Mechanical Systems and Signal Processing (2024) Vol. 224, pp. 111949-111949
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
Kernel Density Regularized Bayesian Learning Framework for Machining Process Anomaly Detection
Zhipeng Ma, Yue Zhang, Xuebin Dai, et al.
Frontiers in artificial intelligence and applications (2024)
Open Access
A Domain-Aware Federated Learning Study for CNC Tool Wear Estimation
Inci Sila Kaleli, Perin Ünal, Bilgin Umut Deveci, et al.
Lecture notes in computer science (2024), pp. 250-265
Closed Access
Estimating Remaining Useful Life of Cutting Tools in Machining Using an Extended Kalman Filter
Qian Yang, Debasish Mishra, Krishna R. Pattipati, et al.
IFAC-PapersOnLine (2024) Vol. 58, Iss. 19, pp. 1264-1269
Open Access
Page 1

Scroll to top