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About

Hongcai Chen’s research bridges the frontiers of swarm robotics and intelligent manufacturing, with a particular focus on self-organizing robotic systems and precision surface roughness prediction. In his early work, Chen developed the Triangle Formation Algorithm (TFA) and the Geometry Based Algorithm (GBA) for swarm aggregations, enabling simple robotic sensors to self-organize into stable geometric formations through decentralized control—foundational contributions to autonomous multi-robot coordination. More recently, Chen has pioneered physics-guided machine learning approaches for manufacturing quality control. His 2025 papers introduce novel hybrid models that combine theoretical knowledge with data-driven techniques, including a physics-guided meta-learning framework and a knowledge-based fuzzy broad learning system, to predict grinding surface roughness under limited data and varying working conditions. These models achieve significant improvements in accuracy and reliability over conventional methods, directly impacting automated grinding processes and production efficiency. With over 18 citations across his most-cited works, Chen’s research demonstrates a clear trajectory from fundamental swarm algorithms to applied intelligent manufacturing—showcasing his versatility in addressing both theoretical and practical challenges in robotics and industrial engineering.