Zesheng Wang

Papers

Total Citations

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About

Zesheng Wang is a researcher at the forefront of intelligent manufacturing and robotic precision machining, with a focus on flexible polishing and adaptive positioning systems. His work addresses critical challenges in automating the finishing of complex, thin-walled components—such as turbine blades—where traditional rigid fixturing falls short. Wang’s most cited paper, “Online positioning of thin-walled blade with small curvature for robotic flexible polishing based on optimal local feature matching” (2025, 5 citations), introduces a novel method that leverages optimal local feature matching to achieve real-time, high-accuracy positioning of delicate parts during robotic polishing. This contribution is pivotal for industries like aerospace and energy, where blade surface integrity directly impacts performance and lifespan. By enabling robots to adaptively locate and polish components with small curvatures, Wang’s work reduces manual intervention and enhances process consistency. Though early in its citation impact, the paper’s innovative integration of computer vision and robotic control signals a promising trajectory. Wang’s research bridges the gap between theoretical optimization and practical automation, offering a scalable solution for high-mix, low-volume production environments. His achievements underscore a commitment to advancing smart manufacturing through precise, data-driven robotic systems.