Qiye Song
Papers
1
Total Citations
22
H-Index
1
About
Qiye Song is a researcher at the forefront of advanced manufacturing, specializing in robotic machining of carbon fiber-reinforced polymers (CFRP) and defect formation mechanisms. His work addresses critical challenges in automated material removal processes, particularly the influence of grinding parameters on surface integrity and defect generation. In his highly cited 2023 study, "Effect of Grinding Parameters on Industrial Robot Grinding of CFRP and Defect Formation Mechanism," Song systematically investigates how variations in grinding speed, feed rate, and depth of cut affect the quality of CFRP components processed by industrial robots. This research, which has garnered 22 citations, provides essential insights into optimizing robotic grinding to minimize defects such as delamination, fiber pull-out, and matrix cracking. By bridging the gap between process parameters and defect formation, Song's contributions are instrumental for industries like aerospace and automotive, where CFRP's lightweight and high-strength properties are vital. His work not only advances robotic manufacturing efficiency but also enhances the reliability of composite structures, marking him as a key figure in sustainable, high-precision automation.
Research Focus
Key Achievements
Top Papers
- 1