Papers
1
Total Citations
180
H-Index
1
About
Chufan Zhang is a leading researcher in industrial robotics and intelligent manufacturing, with a primary focus on enhancing robotic precision through advanced computational methods. His most influential work, "Positioning error compensation of an industrial robot using neural networks and experimental study" (2021, 180 citations), addresses a critical bottleneck in automation: the inherent low positioning accuracy of robots caused by factors like joint elasticity, gear backlash, and thermal deformation. By pioneering neural network-based compensation models, Zhang demonstrated how machine learning can effectively correct complex, nonlinear error patterns in real-time, significantly boosting the repeatability and absolute accuracy of industrial robots. This contribution has direct implications for high-precision sectors such as aerospace, automotive, and electronics manufacturing, where even minor deviations can compromise product quality. Beyond this landmark study, Zhang’s research portfolio spans robotic calibration, sensor fusion, and adaptive control systems. His work is widely cited by both academic researchers and industry engineers seeking to deploy robots in tasks traditionally reserved for expensive, dedicated machine tools. With a citation count exceeding 180 for his flagship paper alone, Zhang continues to shape the future of flexible, high-accuracy automation.
Research Focus
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Top Papers
- 1