Papers
4
Total Citations
141
H-Index
3
About
Miguel Saez is a leading researcher at the intersection of smart manufacturing, the Internet of Things (IoT), and advanced robotics. His most impactful work centers on real-time performance monitoring, where he developed a groundbreaking framework using hybrid simulation to assess manufacturing systems. His seminal 2018 paper on this topic has garnered 122 citations, establishing a foundation for synchronizing virtual environments with physical plant floors to analyze continuous and discrete machine variables. Saez also addresses critical data interoperability challenges, pioneering a data transformation adapter that enables seamless communication between edge devices and cloud computing for smart manufacturing systems. In recent years, he has explored cutting-edge applications of reinforcement learning for robotic manipulation, contributing an influential industrial case study. His work on robot-to-robot collaboration for fixtureless assembly highlights key challenges and opportunities in the automotive industry, pushing toward more flexible, autonomous production lines. Through these contributions, Saez has become a pivotal figure in bridging theoretical advances with practical, data-driven solutions for modern manufacturing.
Research Focus
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