Abhijit Chakraborty

RTX (United States)

Papers

1

Total Citations

31

H-Index

1

About

Abhijit Chakraborty is a leading researcher in robotic manufacturing and process control, with a focus on advancing industrial automation through intelligent force and motion strategies. His work centers on the integration of large industrial robots for precision surface enhancement processes, particularly deep rolling—a technique critical for improving component fatigue life. Chakraborty’s major contribution lies in developing iterative learning control frameworks that enable robots to simultaneously regulate both motion and force, overcoming the inherent limitations of position-controlled industrial arms. His most-cited paper, "Robotic Deep Rolling With Iterative Learning Motion and Force Control" (2020, 31 citations), demonstrates how wrist-mounted force sensors can be leveraged to adjust position setpoints in real time, achieving the compliance needed for consistent surface treatment. This work has significant implications for cost-effective, flexible automation in aerospace and automotive manufacturing. By bridging the gap between high-speed robotic motion and precise force interaction, Chakraborty’s research is shaping the next generation of adaptive manufacturing systems, making him a notable figure in the field of robotic process control.

Research Focus

Key Achievements

1
H-Index
1
Papers
31
Total Citations
31
Avg Citations/Paper
🏆 Most Cited Paper
Robotic Deep Rolling With Iterative Learning Motion and Force Control
31 citations · 2020
📈 Most Prolific Year: 2020 (1 Papers)
🤝 Key Collaborators: 5
🏛 Institutions: RTX (United States)

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
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