Lingbo Zheng

Chongqing University of Science and Technology

Papers

Total Citations

H-Index

About

Lingbo Zheng is a researcher at the forefront of intelligent robotic machining, with a primary focus on adaptive control and force-tracking systems for automated manufacturing. Their most-cited work, "Research on a Method of Robot Grinding Force Tracking and Compensation Based on Deep Genetic Algorithm" (2023, 6 citations), tackles a critical industrial challenge: the mismatch between complex cast workpiece contours and their 3D models, which often causes uneven grinding. Zheng’s major contribution lies in integrating deep genetic algorithms with robotic force control, enabling real-time trajectory compensation that reduces under-grinding and over-grinding errors. This innovation enhances precision in automated finishing processes, directly benefiting high-stakes industries like aerospace and automotive manufacturing. While their citation count is still growing, Zheng’s work has been recognized for its practical impact, offering a scalable solution to a persistent quality-control problem. By bridging evolutionary computation and robotic manipulation, Zheng is helping to advance the next generation of adaptive manufacturing systems, where machines can autonomously adjust to real-world variability. Their research continues to inspire students and engineers seeking to merge AI with industrial robotics for smarter, more reliable production.