Yong-Keun Park

About

Yong-Keun Park is a rising researcher in smart manufacturing and industrial automation, with a focus on digital twin technology, robot-assisted systems, and data-driven predictive maintenance. His work centers on integrating intelligent automation into production environments, particularly through the development of digital twins for machine tending systems that enable plug-and-produce capabilities via ISO 21919 interfaces. Park’s most cited paper (2023, 9 citations) introduces a framework for automating CNC machine tool operations, addressing a critical bottleneck in industrial robotics adoption. He has also contributed to predictive maintenance with a one-stage ensemble framework using convolutional autoencoders for remaining useful life estimation (2022, 2 citations), and explored power consumption data analysis for energy-efficient production planning. While his citation counts are still building, Park’s research is notable for its practical, implementation-oriented approach to Industry 4.0 challenges, bridging the gap between theoretical automation concepts and real-world factory integration. His work on standardized interfaces and machine tending systems holds promise for reducing deployment complexity in smart factories, making him a researcher to watch in the evolving landscape of intelligent manufacturing.

Research Focus

Key Achievements

Top Papers

1. 1
   Development of Digital twin for Plug-and-Produce of a Machine tending system through ISO 21919 interface

   9 citations · 2023
2. 2
   A One-Stage Ensemble Framework Based on Convolutional Autoencoder for Remaining Useful Life Estimation

   2 citations · 2022

Key Collaborators

Contact & Links