Babak Kianmajd
Papers
2
Total Citations
15
H-Index
2
About
Babak Kianmajd is a researcher whose work sits at the critical intersection of robotics, manufacturing, and orthopedic surgery. His primary research focuses on optimizing robotic-assisted arthroplasty, specifically total hip replacement, by developing novel computational methods to improve surgical precision and implant longevity. Kianmajd’s major contribution is the creation of a novel toolpath force prediction algorithm that leverages CAM volumetric data, allowing for the pre-operative optimization of robotic cutting strategies. This work directly addresses a key gap in the field: while surgeons operate the robots, they rarely control the underlying toolpath parameters. By enabling the prediction of forces during milling, his algorithm helps minimize bone damage and improve the fit of prosthetic implants. His most cited paper, "A novel toolpath force prediction algorithm..." (2016, 11 citations), along with his subsequent work on optimal toolpath methodology (2016, 4 citations), provides a foundational framework for automating and refining the robotic cutting process. Kianmajd’s research is essential reading for those interested in the practical, engineering-driven improvements that are making robotic surgery safer, more efficient, and more reproducible.
Research Focus
Key Achievements
Top Papers
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