Dr. Kun-Ying Li is an accomplished researcher in sustainable engineering and intelligent manufacturing, focusing on low-carbon technologies and energy-efficient machine tools. His research encompasses ISO14064-1:2018 greenhouse gas inventory, industrial energy-saving methods, intelligent thermal error analysis, cooling optimization, multi-objective optimization, and precision machinery design. His work emphasizes the integration of reliability engineering, applied mathematics, smart manufacturing systems, and computer-assisted engineering within the framework of Industry 4.0. He has contributed to several industry-academic collaboration projects aimed at optimizing cooling systems for multi-axis machine tools and implementing carbon inventory strategies in manufacturing transformation programs. These projects have involved partnerships with precision machinery and metal manufacturing companies, supported by national funding agencies. His expertise extends to developing innovative approaches for reducing energy consumption, enhancing process reliability, and improving the performance of advanced machine tools. Through a combination of technical insight and practical application, his research supports the transition toward intelligent, sustainable, and high-efficiency production systems that align with global goals for carbon reduction and green industry innovation. He has 220 citations by 151 documents, 29 published documents, and an h-index of 9, reflecting his consistent research contributions and growing academic influence in the fields of precision engineering and green manufacturing.
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Liu, Y. C., Li, K. Y., & Tsai, Y. C. (2021). Spindle thermal error prediction based on LSTM deep learning for a CNC machine tool. Applied Sciences, 11(12), 5444.
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Li, K. Y., Luo, W. J., & Wei, S. J. (2020). Machining accuracy enhancement of a machine tool by a cooling channel design for a built-in spindle. Applied Sciences, 10(11), 3991.
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Hsieh, M. C., Maurya, S. N., Luo, W. J., Li, K. Y., Hao, L., & Bhuyar, P. (2022). Coolant volume prediction for spindle cooler with adaptive neuro-fuzzy inference system control method. Sensors & Materials, 34, 28.
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Li, K. Y., Luo, W. J., Hong, X. H., Wei, S. J., & Tsai, P. H. (2020). Enhancement of machining accuracy utilizing varied cooling oil volume for machine tool spindle. IEEE Access, 8, 28988–29003.
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Li, K. Y., Maurya, S. N., Lee, Y. H., Luo, W. J., Chen, C. N., & Wellid, I. (2023). Thermal deformation and economic analysis of a multi-object cooling system for spindles with varied coolant volume control. The International Journal of Advanced Manufacturing Technology, 126(3), 1807–1821.
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Maurya, S. N., Li, K. Y., Luo, W. J., & Kao, S. Y. (2022). Effect of coolant temperature on the thermal compensation of a machine tool. Machines, 10(12), 1201.