Ruize Sun

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About

Ruize Sun is a pioneering researcher at the intersection of robotics, digital twin technology, and precision manufacturing, with a primary focus on advancing automation for the electronics assembly industry. His work addresses critical challenges in 3C (computer, communication, and consumer electronics) assembly, particularly in the delicate task of flexible printed circuit (FPC) integration. Sun’s major contributions include developing a digital twin-driven measurement framework for robotic FPC assembly, which overcomes the limitations of traditional vision systems when handling miniature connectors and receivers—a problem that has long hindered full automation in mobile phone manufacturing. His most cited work (21 citations) demonstrates how real-time digital twins can enhance detection accuracy and process reliability in these high-precision tasks. Building on this foundation, Sun introduced a digital-twin-assisted skill learning approach (13 citations) that enables robots to acquire complex assembly skills through simulated environments, significantly reducing the gap between human dexterity and robotic precision. His research has direct implications for reducing labor costs and improving efficiency in high-volume electronics production, positioning him as a key contributor to the next generation of smart manufacturing systems.