Papers
3
Total Citations
15
H-Index
2
About
Guijun Ma’s research lies at the intersection of robotic machining, intelligent manufacturing, and data-driven optimization. His major contributions focus on enhancing the precision and efficiency of robotic belt grinding (RBG) through advanced machine learning and multi-objective optimization frameworks. Notably, his 2025 work introduces a novel pairwise domain-adaptation-assisted dual-task learning approach, enabling accurate coprediction of material removal depth and surface roughness across varying parameter spaces—a critical challenge in real-world machining. This paper has already garnered 8 citations, reflecting its immediate impact. Ma also developed a sparse drift identification (SDI) method for force/torque sensor calibration in industrial robots (2024, 6 citations), improving sensor reliability in dynamic environments. His closed-loop parameter optimization strategy, integrating physics-informed machine learning with multiobjective optimization (2025), addresses the bottleneck of simultaneous parameter tuning in time-consuming experiments. Through these contributions, Ma is advancing the frontier of adaptive, high-quality robotic machining, offering practical solutions for industries seeking to automate complex finishing processes with greater consistency and reduced trial-and-error.
Research Focus
Key Achievements
Top Papers
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