Gan Li | Manufacturing Technology | Best Researcher Award

Dr. Gan Li, College of Mechanical and Electronic Engineering, China University of Petroleum, China.

Gan Li is an Assistant Professor at the College of Mechanical and Electronic Engineering, China University of Petroleum, and a key researcher at the National Engineering Research Center of Marine Geophysical Prospecting. His expertise lies in advanced manufacturing technology, ultrasonic machining, process simulation, and quality control. Gan earned his Ph.D. in Mechanical Engineering from Dalian University of Technology (2018–2024) and a dual B.S. in Mechanical Engineering and Engineering Management from the China University of Petroleum (2014–2018). His research on ultra-precision machining has been instrumental in advancing material removal mechanisms and real-time signal analysis.

Publication profile

Scopus

Orcid

Googlescholar

Education and Experience

Ph.D. in Mechanical Engineering (2018–2024)
Dalian University of Technology, GPA 3.83/4.0
B.S. in Mechanical Engineering & Engineering Management (2014–2018)
China University of Petroleum, Ranked Top 4% (7/196)
Assistant Professor
College of Mechanical and Electronic Engineering, China University of Petroleum
Key Researcher
National Engineering Research Center of Marine Geophysical Prospecting

Suitability For The Award

Dr. Gan Li, an Assistant Professor at the College of Mechanical and Electronic Engineering, China University of Petroleum, PR China, and a key member of the National Engineering Research Center of Marine Geophysical Prospecting and Exploration and Development Equipment, is an exceptional candidate for the Best Researcher Award. With a strong foundation in advanced manufacturing technologies and ultra-precision machining, Dr. Li’s innovative research has redefined the frontiers of mechanical engineering, making significant contributions to precision manufacturing, signal analysis, and process simulation.

Professional Development

Gan Li’s professional journey showcases excellence in ultra-precision machining, process simulation, and advanced equipment development. He has led groundbreaking projects such as studying the micro-removal mechanism in tungsten alloys and developing real-time signal monitoring methods for precision machining. His innovative work integrates simulation tools like Abaqus and CPFEM with state-of-the-art analysis techniques, including TEM, EBSD, and XRD. Gan’s dedication has led to improved precision control methods and surface integrity in complex machining processes, advancing cutting-edge research in manufacturing technologies.

Research Focus

Gan Li specializes in advanced manufacturing technology, with a focus on ultra-precision machining, ultrasonic machining, and process simulation. His work emphasizes signal analysis, material removal mechanisms, and surface integrity for hard and brittle materials like tungsten alloys. Leveraging advanced tools such as Abaqus, Lammps, and MATLAB, Gan pioneers innovations in grinding process optimization, digital twin technology, and real-time quality control. His research contributes to next-generation manufacturing processes, enhancing precision, efficiency, and sustainability.

Awards and Honors

Outstanding Senior Thesis (12/89), Jiangsu, China (2023)
Third Prize, Future Flying Machine Innovation Competition, Dalian, China (2019)
Recognition at the 22nd Chinese Conference of Abrasive Technology

Publications Top Notes

Undeformed chip thickness models for precise vertical-spindle face grinding of tungsten heavy alloy
Precision Engineering, 2024, Cited by: 3
Wheel wear of tungsten heavy alloy precision grinding and its influence mechanism on surface quality
Journal of Hunan University Natural Sciences, 2024
Research on grinding wheel wear measurement methods: Current status and future perspectives
Scientia Sinica Technologica, 2024
A grinding force model and surface formation mechanism of cup wheels considering crystallographic orientation
Journal of Materials Processing Technology, 2023, Cited by: 9
An online monitoring methodology for grinding state identification based on real-time signal of CNC grinding machine
Mechanical Systems and Signal Processing, 2023, Cited by: 12
Cutting chatter in ultrasonic elliptical vibration cutting and its influence on surface roughness and tool wear
Metals, 2023, Cited by: 6