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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:

Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network
Yung‐Chih Lin, Kung-Da Wu, Wei-Cheng Shih, et al.
Applied Sciences (2020) Vol. 10, Iss. 11, pp. 3941-3941
Open Access | Times Cited: 85

Showing 1-25 of 85 citing articles:

A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges
Vahid Nasir, Farrokh Sassani
The International Journal of Advanced Manufacturing Technology (2021) Vol. 115, Iss. 9-10, pp. 2683-2709
Closed Access | Times Cited: 203
Modeling and Prediction of Surface Roughness in Hybrid Manufacturing–Milling after FDM Using Artificial Neural Networks
Strahinja Djurović, Dragan Lazarević, Bogdan Ćirković, et al.
Applied Sciences (2024) Vol. 14, Iss. 14, pp. 5980-5980
Open Access | Times Cited: 12
Development of Surface Roughness Prediction and Monitoring System in Milling Process
Yu-Sheng Lai, Lin Wei-Zhu, Yung‐Chih Lin, et al.
Engineering Technology & Applied Science Research (2024) Vol. 14, Iss. 1, pp. 12797-12805
Open Access | Times Cited: 8
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
Prediction of surface roughness in turning using vibration features selected by largest Lyapunov exponent based ICEEMDAN decomposition
Vikrant Guleria, Vivek Kumar, Pradeep Kumar Singh
Measurement (2022) Vol. 202, pp. 111812-111812
Closed Access | Times Cited: 28
Parametric Optimization for Cutting Forces and Material Removal Rate in the Turning of AISI 5140
Mustafa Kuntoğlu, Osman Acar, Munish Kumar Gupta, et al.
Machines (2021) Vol. 9, Iss. 5, pp. 90-90
Open Access | Times Cited: 38
Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time–Frequency-Based Features and Deep Learning Models
Sameer Sayyad, Satish Kumar, Arunkumar Bongale, et al.
Sensors (2023) Vol. 23, Iss. 12, pp. 5659-5659
Open Access | Times Cited: 15
An ensemble neural network for optimising a CNC milling process
Patrick G. Mongan, Eoin P. Hinchy, Noel P. O’Dowd, et al.
Journal of Manufacturing Systems (2023) Vol. 71, pp. 377-389
Closed Access | Times Cited: 13
Research on Intelligent Prediction of Surface Roughness in Cutting 42CrMo Steel by using Particle Swarm Optimization-based Support Vector Machine
HaiYue Zhao, Yan Cao, Gorbachev Sergey, et al.
International Journal of Precision Engineering and Manufacturing (2024)
Closed Access | Times Cited: 5
Applications of artificial neural networks in machining processes: a comprehensive review
Sirin Chakraborty, Shankar Chakraborty
International Journal on Interactive Design and Manufacturing (IJIDeM) (2024) Vol. 18, Iss. 4, pp. 1917-1948
Closed Access | Times Cited: 4
Multi-axis CNC finishing and surface roughness prediction of TC11 titanium alloy open integral micro impeller
HaiYue Zhao, Yan Cao, Junde Guo, et al.
Advances in Mechanical Engineering (2024) Vol. 16, Iss. 4
Open Access | Times Cited: 4
Effect on Machinability Characteristics of Cryogenic Process and Performance Assessment by Using Machine Learning Approach with Scaled Conjugate Gradient Algorithm
Mehmet Akkaş, Mehmet Gürdal
Iranian Journal of Science and Technology Transactions of Mechanical Engineering (2025)
Closed Access
A review of artificial intelligence application for machining surface quality prediction: from key factors to model development
Jeong Hoon Ko, Chen Yin
Journal of Intelligent Manufacturing (2025)
Open Access
Roughness prediction in end milling of Ti1023 alloy using machine learning-based Gaussian Process Regression
Viswajith S. Nair, K. Rameshkumar, S. Saravanamurugan
Procedia Computer Science (2025) Vol. 253, pp. 455-464
Open Access
Surface morphologies and subsurface damage characteristics of plain-woven CFRP composite in UV laser multi-passes scanning
Jinxuan Bai, Hou Yuze, Haiqing Zhu, et al.
Journal of Materials Research and Technology (2025)
Open Access
A Holistic Perspective on Sustainable Machining of Al6082: Synergistic Effects of Nano-Enhanced Bio-Lubricants
Rüstem Binali, Mehmet Erdi Korkmaz, Mehmet Tayyip Özdemir, et al.
Machines (2025) Vol. 13, Iss. 4, pp. 293-293
Open Access
Surface roughness prediction in machining using two-stage domain-incremental learning with input dimensionality expansion
Clayton Cooper, Jianjing Zhang, Y. B. Guo, et al.
Journal of Manufacturing Systems (2025) Vol. 80, pp. 503-510
Open Access
Multi-agent evolution reinforcement learning method for machining parameters optimization based on bootstrap aggregating graph attention network simulated environment
Weiye Li, Songping He, Xinyong Mao, et al.
Journal of Manufacturing Systems (2023) Vol. 67, pp. 424-438
Closed Access | Times Cited: 12
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
Roughness prediction of end milling surface for behavior mapping of digital twined machine tools
Suiyan Shang, Gedong Jiang, Zheng Sun, et al.
Digital Twin (2024) Vol. 3, pp. 4-4
Open Access | Times Cited: 3
Research on surface roughness detection and prediction of ti-6Al-4v titanium alloy based on multi-feature fusion
Minghui Shao, Songyuan Li, Youyong Li, et al.
Machining Science and Technology (2024) Vol. 28, Iss. 3, pp. 299-322
Closed Access | Times Cited: 3
Analysis and optimization of machining parameters of AZ91 alloy nanocomposite with the Influences of nano ZrO2 through vacuum diecast process
R. Venkatesh, Ismail Hossain, V. Mohanavel, et al.
Heliyon (2024) Vol. 10, Iss. 15, pp. e34931-e34931
Open Access | Times Cited: 3
Multi-objective optimization of machining factors on surface roughness, material removal rate and cutting force on end-milling using MWCNTs nano-lubricant
Imhade P. Okokpujie, Olayinka S. Ohunakin, Christian A. Bolu
Progress in Additive Manufacturing (2021) Vol. 6, Iss. 1, pp. 155-178
Closed Access | Times Cited: 22
Surface roughness prediction using multi-source heterogeneous data and Bayesian quantile regression in milling process
Weichao Liu, Pengyu Wang, Youpeng You
Journal of Manufacturing Processes (2023) Vol. 95, pp. 446-460
Closed Access | Times Cited: 8
Multi-objective Optimization of CNC Milling Parameters of 7075 Aluminium Alloy Using Response Surface Methodology
Md. Hasan Ali, Chi Cong, L.-S. Van, et al.
Jordan Journal of Mechanical and Industrial Engineering (2023) Vol. 17, Iss. 03, pp. 393-402
Open Access | Times Cited: 8
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