Jinting Yu

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About

Jinting Yu is a researcher specializing in advanced manufacturing and robotic precision machining, with a particular focus on the optimization of robotic belt grinding for complex geometries. Their most-cited work, "Optimization method and experimental research on robot belt grinding trajectory of additive blade with non-uniform allowance distribution" (2024, 9 citations), addresses a critical challenge in additive manufacturing: achieving high-precision surface finishing for components like blades, which often have non-uniform material allowances. Yu’s contributions lie in developing trajectory optimization methods that enhance grinding accuracy and efficiency, bridging the gap between additive and subtractive processes. This work has immediate implications for aerospace and energy sectors, where blade quality directly impacts performance. Though early in their citation impact, Yu’s research is notable for its experimental rigor and practical relevance, offering a systematic approach to robotic path planning that reduces manual intervention. Their achievements include advancing the integration of digital twin concepts and real-time feedback in manufacturing, positioning them as an emerging voice in intelligent robotic processing. For students and researchers, Yu’s work exemplifies how algorithmic innovation can solve real-world production bottlenecks.