Papers
20
Total Citations
659
H-Index
15
About
Xukai Ren is a distinguished researcher specializing in robotic belt grinding, advanced manufacturing processes, and intelligent sensing systems, with a particular focus on high-performance superalloys such as Inconel 718. His work has fundamentally advanced the understanding of material removal mechanisms, thermal dynamics, and process optimization in automated grinding systems. Ren's foundational contribution — a local process model for robotic belt grinding simulation, published in 2006 and accumulating 109 citations — established a rigorous theoretical framework that continues to underpin the field. Building on this, he pioneered machine learning-driven approaches to material removal prediction, integrating acoustic sensing with ensemble XGBoost algorithms and later leveraging worn-belt imaging with CatBoost, demonstrating a consistent commitment to intelligent, data-driven manufacturing. His point cloud-based methods for weld seam reconstruction and trajectory generation have opened new frontiers in adaptive robotic path planning for complex freeform surfaces, earning over 130 combined citations across related publications. With a body of work spanning process modeling, energy partitioning, and thermal monitoring, Ren's research has garnered over 530 citations collectively. His 2023 review of robotic belt grinding of superalloys further cements his authority as a leading synthesizer and innovator in precision advanced manufacturing.
Research Focus
Key Achievements
Top Papers
- 1A local process model for simulation of robotic belt grinding109 citations · 2006
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- 9A review of recent advances in robotic belt grinding of superalloys29 citations · 2023
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