OpenAlex Citation Counts

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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:

COMPLEXITY-BASED ANALYSIS OF THE RELATION BETWEEN TOOL WEAR AND MACHINE VIBRATION IN TURNING OPERATION
CHAI LIP KIEW, AKSHAYEN BRAHMANANDA, KH. TAUHID ISLAM, et al.
Fractals (2019) Vol. 28, Iss. 01, pp. 2050018-2050018
Closed Access | Times Cited: 16

Showing 16 citing articles:

Physiological State and Learning Ability of Students in Normal and Virtual Reality Conditions: Complexity-Based Analysis
Mohammad Hossein Babini, Vladimír Kulish, Hamidreza Namazi
Journal of Medical Internet Research (2020) Vol. 22, Iss. 6, pp. e17945-e17945
Open Access | Times Cited: 40

Deep Learning Tool Wear State Identification Method Based on Cutting Force Signal
Shuhang Li, Meiqiu Li, Yingning Gao
Sensors (2025) Vol. 25, Iss. 3, pp. 662-662
Open Access

The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation
Mohamed Lamine Bouhalais, Mourad Nouioua
The International Journal of Advanced Manufacturing Technology (2021) Vol. 115, Iss. 9-10, pp. 2989-3001
Open Access | Times Cited: 31

Analysis of tool vibration and surface roughness during turning process of tempered steel samples using Taguchi method
Menderes Kam, Mustafa Demirtaş
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (2021) Vol. 235, Iss. 5, pp. 1429-1438
Closed Access | Times Cited: 21

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

Decoding of the Relationship between Brain and Facial Muscle Activities in Response to Dynamic Visual Stimuli
Mirra Soundirarajan, Mohammad Hossein Babini, Sue Sim, et al.
Fluctuation and Noise Letters (2020) Vol. 19, Iss. 04, pp. 2050041-2050041
Closed Access | Times Cited: 18

Complexity-based decoding of the relation between human voice and brain activity
Mohamed Rasmi Ashfaq Ahamed, Mohammad Hossein Babini, Hamidreza Namazi
Technology and Health Care (2020) Vol. 28, Iss. 6, pp. 665-674
Closed Access | Times Cited: 15

ANÁLISE DA USINAGEM POR TORNEAMENTO UM CONECTOR DE ENGATE RÁPIDO PARA AR COMPRIMIDO
MATHEUS COSTA PEREIRA, PEDRO JOSÉ PAPANDRÉA
Revista Científica e-Locução (2024) Vol. 1, Iss. 25, pp. 34-34
Open Access

The pin tool wear identification with vibration signal of friction stir lap welding based on a new pin tool wear division model
Yuning Wang, Siyu Zhao, Peng Zhang, et al.
Measurement (2024) Vol. 242, pp. 116131-116131
Closed Access

Research on the coupling characteristics of energy power density of cutting vibration and cutting heat based on the particle swarm optimization algorithm
Songyuan Li, Shuncai Li, Yuqing Li, et al.
Transactions of the Canadian Society for Mechanical Engineering (2022) Vol. 46, Iss. 3, pp. 614-625
Closed Access | Times Cited: 1

Multi-domain Features Fusion Adaptive Neural Network Tool Wear Recognition Model
Hanyang Wang, Ming Luo, Fengshou Gu
Mechanisms and machine science (2023), pp. 751-765
Closed Access

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