Michael A. Klecka
Papers
1
Total Citations
31
H-Index
1
About
Michael A. Klecka is a leading researcher in advanced manufacturing and robotic surface enhancement, with a particular focus on deep rolling processes for industrial applications. His most-cited work, "Robotic Deep Rolling With Iterative Learning Motion and Force Control" (2020, 31 citations), addresses a critical challenge in modern manufacturing: enabling large, cost-effective industrial robots to perform precise force-controlled operations traditionally reserved for specialized machinery. Klecka's major contribution lies in developing iterative learning control algorithms that allow robots to dynamically adjust position setpoints based on real-time force feedback from wrist-mounted sensors, effectively bridging the gap between robotic flexibility and the stringent demands of surface treatment processes. This work has significant implications for industries seeking to reduce costs while maintaining high-quality surface finishing, particularly in aerospace and automotive components. His research demonstrates how adaptive control strategies can transform standard industrial robots into versatile tools for precision manufacturing, opening new pathways for automated surface enhancement that were previously unattainable with conventional robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Robotic Deep Rolling With Iterative Learning Motion and Force Control31 citations · 2020