Leaderboards/Advanced machining processes and optimization

Advanced machining processes and optimization

Last updated July 7, 2026

Rising Stars

New and hot

Rising Stars Researchers

1
Zhenjing Duan
Dalian University of Technology
2
Jia Ge
Northwestern Polytechnical University
3
Tony Z. Zhao
Stanford University
4
Andrea la Monaca
Rolls-Royce (United Kingdom)
5
Anandaram Mandyam N
Bangalore University
6
Jingyan Xia
South China University of Technology
7
Dilli Ganesh
Saveetha University
8
Runqiong Wang
Ministry of Education of the People's Republic of China
9
Hao Su
North China Electric Power University
10
Andrea De Bartolomeis
University of Bath

Rising Stars Papers

1
A digital twin-driven approach for partial domain fault diagnosis of rotating machinery
Engineering Applications of Artificial Intelligence|2024
78 cit.
2
Application of deep learning to fault diagnosis of rotating machineries
Measurement Science and Technology|2024
68 cit.
3
Digital Twin-Assisted Fault Diagnosis of Rotating Machinery Without Measured Fault Data
IEEE Transactions on Instrumentation and Measurement|2024
61 cit.
4
Toward digital twins for high-performance manufacturing: Tool wear monitoring in high-speed milling of thin-walled parts using domain knowledge
Robotics and Computer-Integrated Manufacturing|2024
56 cit.
5
Tool wear mechanisms in cold plasma and nano-lubricant multi-energy field coupled micro-milling of Al-Li alloy
Tribology International|2024
50 cit.
6
Thermal effect in CFRP machining: Temperature field characteristics, heat generation mechanism and thermal damage management
Composite Structures|2025
33 cit.
7
Cold plasma and different nano-lubricants multi-energy field coupling-assisted micro-milling of Al-Li alloy 2195-T8 and flow rate optimization
Journal of Manufacturing Processes|2024
29 cit.
8
Tribological and micro-milling performance of surfactant-free microwave plasma-modified Al2O3 nanoparticles biodegradable lubricants
Journal of Cleaner Production|2025
28 cit.
9
Tackling data scarcity in machine learning-based CFRP drilling performance prediction through a broad learning system with virtual sample generation (BLS-VSG)
Composites Part B Engineering|2025
27 cit.
10
Milling surface roughness monitoring using real-time tool wear data
International Journal of Mechanical Sciences|2024
26 cit.

Emerging Leaders

Gaining momentum

Emerging Leaders Researchers

1
Lida Zhu
Northeastern University
2
Mozammel Mia
Imperial College London
3
Navneet Khanna
Institute of Infrastructure Technology Research and Management
4
Viktor Slavkovikj
Ghent University
5
Sudhansu Ranjan Das
Veer Surendra Sai University of Technology
6
Anant Kumar Singh
Thapar Institute of Engineering & Technology
7
Aqib Mashood Khan
Nanjing University of Aeronautics and Astronautics
8
Guijian Xiao
Chongqing University
9
Daxi Geng
Beihang University
10
Khaled Giasin
University of Portsmouth

Emerging Leaders Papers

1
Toward sustainable future: Strategies, indicators, and challenges for implementing sustainable production systems
Sustainable materials and technologies|2023
159 cit.
2
Sustainable cooling strategies to reduce tool wear, power consumption and surface roughness during ultrasonic assisted turning of Ti-6Al-4V
Tribology International|2022
156 cit.
3
Effect of mixing method and particle size on hardness and compressive strength of aluminium based metal matrix composite prepared through powder metallurgy route
Journal of Materials Research and Technology|2022
125 cit.
4
Influence of MoS2 and graphite-reinforced nanofluid-MQL on surface roughness, tool wear, cutting temperature and microhardness in machining of Inconel 625
CIRP journal of manufacturing science and technology|2023
112 cit.
5
Tool wear prediction in face milling of stainless steel using singular generative adversarial network and LSTM deep learning models
The International Journal of Advanced Manufacturing Technology|2022
99 cit.
6
Experimental Investigation of Effect of Fiber Length on Mechanical, Wear, and Morphological Behavior of Silane-Treated Pineapple Leaf Fiber Reinforced Polymer Composites
Fibers|2022
91 cit.
7
Hierarchical Structure by Self‐sedimentation of Liquid Metal for Flexible Sensor Integrating Pressure Detection and Triboelectric Nanogenerator
Advanced Functional Materials|2024
78 cit.
8
Analysis of vegetable oil-based nano-lubricant technique for improving machinability of Inconel 690
Journal of Manufacturing Processes|2022
76 cit.
9
Review of serrated chip characteristics and formation mechanism from conventional to additively manufactured titanium alloys
Journal of Alloys and Compounds|2023
76 cit.
10
State-of-the-art review of applications of image processing techniques for tool condition monitoring on conventional machining processes
The International Journal of Advanced Manufacturing Technology|2023
74 cit.

Current Senior Leaders

Established & currently active

Current Senior Leaders Researchers

1
Yusuf Altıntaş
University of British Columbia
2
Changhe Li
Qingdao University of Science and Technology
3
J. Paulo Davim
University of Aveiro
4
Steven Y. Liang
Georgia Institute of Technology
5
Erhan Budak
Sabancı Üniversitesi
6
I.S. Jawahir
University of Kentucky
7
P. Venkateswara Rao
Indian Institute of Technology Delhi
8
Han Ding
Inner Mongolia University
9
Konrad Wegener
ETH Zurich
10
Wenfeng Ding
Taiwan Semiconductor Manufacturing Company (China)

Current Senior Leaders Papers

1
Analytical Prediction of Stability Lobes in Milling
CIRP Annals|1995
1.9k cit.
2
Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design
Applied Mechanics Reviews|2001
1.8k cit.
3
Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels
CIRP journal of manufacturing science and technology|2010
930 cit.
4
Surface integrity in material removal processes: Recent advances
CIRP Annals|2011
904 cit.
5
Thermal issues in machine tools
CIRP Annals|2012
882 cit.
6
Chatter Stability of Metal Cutting and Grinding
CIRP Annals|2004
827 cit.
7
Prediction of Milling Force Coefficients From Orthogonal Cutting Data
Journal of Manufacturing Science and Engineering|1996
802 cit.
8
Recent advances in modelling of metal machining processes
CIRP Annals|2013
771 cit.
9
Analytical Prediction of Chatter Stability in Milling—Part I: General Formulation
Journal of Dynamic Systems Measurement and Control|1998
690 cit.
10
Machine tool feed drives
CIRP Annals|2011
635 cit.

Reagents for Advanced machining processes and optimization

Citation-validated products used in this field

🔥 Trending

Gaining adoption fast

⭐ Top Rated

Best all-around

Cell Signaling TechnologyEvidence 64

CD79B (D5K3Q) Rabbit Monoclonal Antibody (BSA and Azide Free)

#71713$865
View Evidence →
Cell Signaling TechnologyEvidence 67

GPRIN1 (E8A7B) Rabbit Monoclonal Antibody

#62990$161,351
View Evidence →

🌟 All-Time

Most validated

Cell Signaling TechnologyEvidence 64

Phospho-Met (Tyr1003) Matched Antibody Pair

Cited in 10 papers
#72698$1,726
View Evidence →
Cell Signaling Technology

Phospho-VCP (Ser13) Antibody

Cited in 8 papers
#87525$362
View Evidence →
Cell Signaling TechnologyEvidence 64

Stat2 (D9J7L) Rabbit Monoclonal Antibody (InTraSeq™ 3' Conjugate 3093)

Cited in 6 papers
#84800$434
View Evidence →