Papers
3
Total Citations
35
H-Index
3
About
Chengle Bao is an emerging researcher specializing in robotic manufacturing, intelligent process optimization, and precision surface finishing, with a particular focus on the complex challenges of robotic belt grinding for high-performance components. His work addresses a critical transition in modern manufacturing: the shift from labor-intensive manual grinding to automated robotic systems capable of handling geometrically intricate workpieces such as aerospace turbine blades. Bao's most impactful contribution lies in developing predictive models for material removal characteristics during robotic belt grinding of complex blade profiles, work that has garnered 18 citations since its 2023 publication and reflects a sustained research trajectory dating back to earlier foundational studies. By leveraging digital processing techniques, he has helped unlock new possibilities for optimizing grinding precision and consistency. His 2024 study on multi-algorithm fusion–based intelligent decision-making further demonstrates his commitment to integrating machine learning and computational intelligence into real-world manufacturing workflows, already accumulating 14 citations within its first year. Collectively, Bao's research bridges the gap between theoretical modeling and practical industrial application, offering manufacturers reliable frameworks for achieving tighter tolerances and improved surface quality in automated grinding environments — a contribution of growing significance to aerospace and precision engineering sectors.
Research Focus
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