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 Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends
Mustafa Kuntoğlu, Abdullah Aslan, Danil Yurievich Pimenov, et al.
Sensors (2020) Vol. 21, Iss. 1, pp. 108-108
Open Access | Times Cited: 223

Showing 1-25 of 223 citing articles:

Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review
Danil Yurievich Pimenov, Andrés Bustillo, Szymon Wojciechowski, et al.
Journal of Intelligent Manufacturing (2022) Vol. 34, Iss. 5, pp. 2079-2121
Closed Access | Times Cited: 220
Multi-fault diagnosis of Industrial Rotating Machines using Data-driven approach : A review of two decades of research
Shreyas Gawde, Shruti Patil, Satish Kumar, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106139-106139
Open Access | Times Cited: 123
Application of measurement systems in tool condition monitoring of Milling: A review of measurement science approach
Danil Yurievich Pimenov, Munish Kumar Gupta, Leonardo Rosa Ribeiro da Silva, et al.
Measurement (2022) Vol. 199, pp. 111503-111503
Open Access | Times Cited: 96
Tool Condition Monitoring for High-Performance Machining Systems—A Review
Ayman Mohamed, Mahmoud Hassan, Rachid M’Saoubi, et al.
Sensors (2022) Vol. 22, Iss. 6, pp. 2206-2206
Open Access | Times Cited: 88
Intelligent Fault Diagnosis of Rolling Bearing Based on Gramian Angular Difference Field and Improved Dual Attention Residual Network
Anshi Tong, Jun Zhang, Liyang Xie
Sensors (2024) Vol. 24, Iss. 7, pp. 2156-2156
Open Access | Times Cited: 15
Review of advanced sensor system applications in grinding operations
Danil Yurievich Pimenov, Leonardo Rosa Ribeiro da Silva, Mustafa Kuntoğlu, et al.
Journal of Advanced Research (2025)
Open Access | Times Cited: 1
Statistical analysis and predictive modeling of cutting parameters in EN-GJL-250 cast iron turning: application of machine learning and MOALO optimization
Omar Reffas, Haithem Boumediri, Yacine Karmi, et al.
The International Journal of Advanced Manufacturing Technology (2025)
Closed Access | Times Cited: 1
Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling
Mohsen Marani, Mohammadjavad Zeinali, Victor Songmené, et al.
Measurement (2021) Vol. 177, pp. 109329-109329
Closed Access | Times Cited: 81
Tool wear, surface roughness, cutting temperature and chips morphology evaluation of Al/TiN coated carbide cutting tools in milling of Cu–B–CrC based ceramic matrix composites
Üsame Ali Usca, Mahir Uzun, Serhat Şap, et al.
Journal of Materials Research and Technology (2021) Vol. 16, pp. 1243-1259
Open Access | Times Cited: 81
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
A state-of-the-art review on sensors and signal processing systems in mechanical machining processes
Mustafa Kuntoğlu, Emin Salur, Munish Kumar Gupta, et al.
The International Journal of Advanced Manufacturing Technology (2021) Vol. 116, Iss. 9-10, pp. 2711-2735
Closed Access | Times Cited: 78
A review on conventional and advanced minimum quantity lubrication approaches on performance measures of grinding process
Munish Kumar Gupta, Aqib Mashood Khan, Qinghua Song, et al.
The International Journal of Advanced Manufacturing Technology (2021) Vol. 117, Iss. 3-4, pp. 729-750
Closed Access | Times Cited: 70
Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders
Zhaopeng He, Tielin Shi, Jianping Xuan
Measurement (2022) Vol. 190, pp. 110719-110719
Closed Access | Times Cited: 66
Tool wear prediction in face milling of stainless steel using singular generative adversarial network and LSTM deep learning models
Milind Shah, Vinay Vakharia, Rakesh Chaudhari, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 121, Iss. 1-2, pp. 723-736
Open Access | Times Cited: 65
Tool wear and machinability investigations in dry turning of Cu/Mo-SiCp hybrid composites
Emine Şap, Üsame Ali Usca, Munish Kumar Gupta, et al.
The International Journal of Advanced Manufacturing Technology (2021) Vol. 114, Iss. 1-2, pp. 379-396
Closed Access | Times Cited: 64
Indirect monitoring of machining characteristics via advanced sensor systems: a critical review
Mehmet Erdi Korkmaz, Munish Kumar Gupta, Zhixiong Li, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 120, Iss. 11-12, pp. 7043-7078
Closed Access | Times Cited: 63
Machine learning and IoT-based approach for tool condition monitoring: A review and future prospects
Minh‐Quang Tran, Hoang-Phuong Doan, Viet Q. Vu, et al.
Measurement (2022) Vol. 207, pp. 112351-112351
Closed Access | Times Cited: 63
Parametric Optimization for Improving the Machining Process of Cu/Mo-SiCP Composites Produced by Powder Metallurgy
Emine Şap, Üsame Ali Usca, Munish Kumar Gupta, et al.
Materials (2021) Vol. 14, Iss. 8, pp. 1921-1921
Open Access | Times Cited: 60
Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
Giuseppe Ciaburro, Gino Iannace
Applied Sciences (2022) Vol. 12, Iss. 20, pp. 10476-10476
Open Access | Times Cited: 55
Advance monitoring of hole machining operations via intelligent measurement systems: A critical review and future trends
Rüstem Binali, Mustafa Kuntoğlu, Danil Yurievich Pimenov, et al.
Measurement (2022) Vol. 201, pp. 111757-111757
Open Access | Times Cited: 48
Investigation of machinability of Ti–B-SiCp reinforced Cu hybrid composites in dry turning
Serhat Şap, Mahir Uzun, Üsame Ali Usca, et al.
Journal of Materials Research and Technology (2022) Vol. 18, pp. 1474-1487
Open Access | Times Cited: 47
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
A Comparative Review of Thermocouple and Infrared Radiation Temperature Measurement Methods during the Machining of Metals
Emilios Leonidas, Sabino Ayvar-Soberanis, Hatim Laalej, et al.
Sensors (2022) Vol. 22, Iss. 13, pp. 4693-4693
Open Access | Times Cited: 42
Estimation, optimization and analysis based investigation of the energy consumption in machinability of ceramic-based metal matrix composite materials
Üsame Ali Usca, Serhat Şap, Mahir Uzun, et al.
Journal of Materials Research and Technology (2022) Vol. 17, pp. 2987-2998
Open Access | Times Cited: 41
A critical review on tool wear mechanism and surface integrity aspects of SiCp/Al MMCs during turning: prospects and challenges
Rashid Ali Laghari, Muhammad Jamil, Asif Ali Laghari, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 126, Iss. 7-8, pp. 2825-2862
Closed Access | Times Cited: 25
Page 1 - Next Page

Scroll to top