Dr Stephen Lee

Postdoctoral Researcher

Biography

Stephen Lee is a Postdoctoral Researcher with a strong foundation in aerospace engineering and advanced manufacturing technologies. He recently completed his PhD at the University of Limerick’s School of Engineering under the supervision of Prof. Conor McCarthy, Prof. Noel O’Dowd, and Dr. Eoin Hinchy. His doctoral research, titled “Real-Time Digital Twin and Machine Learning Solutions for Hole Quality and Tool Condition Monitoring in Robotic Drilling of Composite Materials,” focused on developing intelligent, real-time monitoring systems to enhance precision and efficiency in composite manufacturing. Stephen holds a B.Sc. in Engineering from the University of Limerick and a technical training certificate in aircraft maintenance from Xiamen, China, certified by the European Aviation Safety Agency (EASA). His undergraduate work included a final-year project on the mechanical performance of composite aerospace parts in high humidity environments, which sparked his interest in composite materials and led to his first research assistantship. Throughout his academic career, Stephen has gained hands-on experience in composite manufacturing, mechanical testing, and SEM analysis. He has published multiple journal papers, including a digital twin framework for robotic drilling and a collaborative study with the University of Luxembourg on hybrid machine learning models for quality assessment. His most recent innovation—a machine learning model for in-situ evaluation of drilling quality and tool condition—has significantly reduced production time and improved cost efficiency in industrial settings. Stephen has also contributed to academia through teaching assistant roles in engineering mechanics, technical drawing, and informatics, and has mentored MSc and BSc students in both laboratory and academic writing tasks. His current research continues to explore the integration of digital twin technology and machine learning in smart manufacturing environments.

Research Interests

Digital twin
Machining processes
Composite material, Manufacturing

Selected Publications

Lee, S. K., Mongan, P. G., Farhadi, A., Hinchy, E. P., O’Dowd, N. P., & McCarthy, C. T. (2025). In-situ evaluation of hole quality and cutting tool condition in robotic drilling of composite materials using machine learning. Journal of Intelligent Manufacturing, 1-22.
Lee, S. K., Simeth, A., Hinchy, E. P., Plapper, P., O’Dowd, N. P., & McCarthy, C. T. (2023). A vision-based hole quality assessment technique for robotic drilling of composite materials using a hybrid classification model. The International Journal of Advanced Manufacturing Technology, 129(3), 1249-1258.