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About

Zheng Fan is a leading researcher in robotic machining, with a core focus on enhancing the precision and stability of industrial robots for milling operations. His work directly addresses a fundamental challenge in the field: the inherent low stiffness of robotic structures, which leads to vibration and deformation errors that compromise machining accuracy. Fan’s major contributions center on developing predictive models and optimization strategies for posture-dependent dynamics. His most influential work, a 2018 paper on stiffness-based posture and feed orientation optimization, has garnered 168 citations, establishing a foundational method for improving robotic milling performance. He further advanced the field with rapid prediction techniques for tool-tip frequency response functions (FRF) and posture-dependent stability prediction using inverse distance weighted methods. These contributions enable more stable and accurate robotic machining by allowing operators to select optimal postures and cutting conditions. Fan has also pioneered methods for deformation error prediction and compensation in multi-axis milling, as well as operational impact excitation techniques for identifying FRF under movement states. With a growing citation record exceeding 280 total citations, Zheng Fan’s research is essential reading for engineers and researchers working to make robotic milling a viable, high-precision alternative to traditional CNC machining.