Kehui Hu

Papers

Total Citations

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About

Kehui Hu is a leading researcher in advanced manufacturing, specializing in robotic additive manufacturing and computational geometry for process optimization. Their work addresses critical challenges in 3D printing, particularly for complex, non-planar geometries. Hu’s most-cited paper, "Optimization-based non-equidistant toolpath planning for robotic additive manufacturing with non-underfill orientation" (2023, 17 citations), introduces a novel approach to toolpath generation that minimizes material waste and structural defects by dynamically adjusting deposition paths and orientation. This contribution is pivotal for industries requiring high-precision, large-scale components, such as aerospace and biomedical engineering. By integrating optimization algorithms with robotic kinematics, Hu enables more efficient, defect-free fabrication of overhanging and curved structures—a long-standing hurdle in additive manufacturing. Their work has garnered attention for its practical impact, offering a scalable solution that bridges the gap between theoretical path planning and real-world robotic control. Hu’s research continues to push the boundaries of automated fabrication, making them a key figure in the evolution of smart manufacturing technologies.