Dayu Guan

Papers

Total Citations

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About

Dayu Guan is a researcher focused on advancing intelligent manufacturing and robotic automation, with key contributions in process monitoring and precision detection for industrial applications. His work addresses critical challenges in robotic grinding, where he pioneered an online monitoring method using proprioceptive signals—such as force and torque feedback—to track actual grinding depth in real time. This approach eliminates the need for costly external sensors, improving surface quality and suppressing abnormal conditions in flexible robotic systems. His 2023 paper on this topic has garnered early citations, reflecting its relevance to adaptive control in manufacturing. Guan also tackles the intricate problem of tiny screw and screw hole detection for automated maintenance, developing a deep learning and machine vision framework that achieves high accuracy in disassembly and assembly tasks. His 2022 study on this subject demonstrates the integration of convolutional neural networks with visual data, offering a robust solution for precision automation. With a growing citation impact, Guan’s work is shaping the future of sensorless robotic control and computer vision in industrial settings, making him a notable contributor to the field of intelligent robotics.