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About

Hongrui Cao is a leading researcher in robotic machining, whose work directly addresses the critical challenges of precision and stability in industrial robotics. His primary research areas include chatter suppression, dynamic modeling, and process optimization for robotic milling. Cao’s major contributions lie in developing intelligent, real-time solutions to overcome the inherent compliance of industrial robots, which often leads to damaging vibrations during machining. His most impactful work introduces a novel magnetorheological fluid (MRF) absorber for low-frequency chatter suppression, a breakthrough that has garnered 17 citations since 2024. Complementing this, his industry-oriented digital twin model for predicting posture-dependent frequency response functions (FRFs) has been cited 18 times, showcasing its practical value. Cao has also pioneered methods for optimizing robot posture and spindle speed to enhance machining stability, and developed advanced techniques for identifying pose-dependent cutting forces, a critical step for improving robot performance. By tackling the fundamental issue of pose-dependent dynamics, his research is directly enabling the wider adoption of robots for high-precision machining tasks, bridging the gap between industrial robot flexibility and CNC-level accuracy.