Dr. Kun-Ying Li is an Associate Professor in the Department of Intelligent Automation Engineering at National Chin-Yi University of Technology, Taiwan, and a leading researcher in intelligent precision manufacturing, low-carbon machine tool technology, and industrial energy-saving systems. He received his Ph.D. in Precision Manufacturing Technology and has accumulated extensive experience spanning academia, national research institutes, and large-scale industry–academic collaborations.

Dr. Li’s research focuses on intelligent thermal error control of machine tools, advanced cooling system design, multi-objective optimization, and low-carbon sustainable manufacturing technologies, with particular emphasis on improving machining accuracy while reducing energy consumption and carbon emissions. He has played a pioneering role in integrating AI-based learning methods, adaptive cooling control, and numerical simulation to enhance the thermal stability and reliability of high-precision machine tools. His work directly supports the global transition toward smart, energy-efficient, and sustainable manufacturing systems. His collaborations span machine tool manufacturers, semiconductor supply chains, and industrial technology research institutions, demonstrating advanced research outcomes that are translated into practical industrial applications.

Dr. Li has published several SCI-indexed international journals and holds multiple international and domestic patents related to thermal stabilization, intelligent cooling control, and machine tool systems. His contributions have been recognized through numerous national and international awards, including best paper awards, invention medals, and outstanding research honors. He is also a reviewer for several international and national journals.

The Intelligent Low-Carbon Precision Manufacturing (ILCPM) Laboratory is dedicated to advancing next-generation manufacturing technologies that integrate high-precision engineering, intelligent control, energy efficiency, and low-carbon sustainability. The laboratory focuses on addressing critical challenges in modern manufacturing systems, particularly those arising from thermal deformation, energy consumption, and carbon emissions in high-precision machine tools.

Building upon extensive expertise in machine tool thermal error modeling, advanced cooling system design, and intelligent optimization, the laboratory develops innovative solutions that enhance machining accuracy, system reliability, and operational sustainability. Core research activities include intelligent spindle and rotary table cooling, thermal–fluid–structure interaction analysis, AI-based thermal prediction and compensation, and multi-objective optimization of energy and carbon performance in manufacturing systems.

The laboratory maintains close industry–academia collaboration with machine tool manufacturers, precision machinery companies, semiconductor-related industries, and national research institutes. Through government-funded and industrial projects, research outcomes are systematically converted into practical engineering solutions, including adaptive cooling control systems, intelligent monitoring platforms, and patented thermal stabilization technologies.

Latest Publications:

1. Lin, Tze-Yin, and Kun-Yin Li. "Cooling System Optimization of Five-Axis Machine Tool Rotary Table for Improved Thermal Accuracy and Energy Efficiency." Case Studies in Thermal Engineering (2026): 107647.
2. Li, Kun-Ying, and Cheng-Kai Huang. "Efficient and precise laser-assisted calibration of virtual tool center points for robotic systems." The International Journal of Advanced Manufacturing Technology 139, no. 7 (2025): 3609-3635.
3. Huang, Cheng-Kai, Tsung-Chia Chen, Kun-Ying Li, Yuan-Hong Tsai, and Swami Nath Maurya. "Optimization of Machine Tool Spindle Cooling for Enhancement of Thermal Prediction Accuracy and Energy Efficiency." International Journal of Precision Engineering and Manufacturing-Green Technology (2025): 1-20.
4. Li, Kun-Ying, Kai-Yuan Ji, and Irsal Pebri. "The active cooling method for improving the accuracy of rotary tables of five-axis machines." Numerical heat transfer, part a: applications 86, no. 12 (2025): 4180-4202.
5. Huang, Cheng-Kai, Chun-Hao Chen, Kun-Ying Li, and Shih-Jie Wei. "Servo Sensor Signal Utilization in Machine Tool Condition Monitoring and Fault Diagnosis." Sensors & Materials 36 (2024).
6. Chen, Chun-Hao, and Kun-Ying Li. "Application of Reliability and Quality Methods to Improving Mean Time Between Failures of Machine Tool Cooling System." Sensors & Materials 35 (2023).
7. Li, Kun-Ying, Ping-Cheng Hsieh, Jen-Ji Wang, and Shih-Jie Wei. "Optimization control method of intelligent cooling and lubrication for a geared spindle." International Journal of Precision Engineering and Manufacturing 24, no. 10 (2023): 1753-1769.
8. Huang, C. K., and Kun-Ying Li. "Automatic calibration method for the relationship between a robotic arm and a two-dimensional profile sensor." In 2023 4th International Conference on Artificial Intelligence, Robotics and Control (AIRC), pp. 70-77. IEEE, 2023.
9. Li, Kun-Ying, Swami Nath Maurya, Yi-Hsien Lee, Win-Jet Luo, Chun-Nan Chen, and Ismail Wellid. "Thermal deformation and economic analysis of a multiobject cooling system for spindles with varied coolant volume control." The International Journal of Advanced Manufacturing Technology 126, no. 3 (2023): 1807-1825.