Suveen Emmanuel

Papers

Total Citations

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About

Suveen Emmanuel is a researcher focused on advancing manufacturing automation through the integration of collaborative robotics and metrology. His work centers on developing low-cost, accessible solutions for industrial inspection tasks, traditionally reliant on human observation. Emmanuel’s most-cited study, “Automated Scanning Techniques Using UR5” (2019, 8 citations), demonstrates how the Universal Robots UR5—a lightweight, collaborative robot—can perform scanning operations with moderate accuracy, offering a cost-effective alternative for part inspection in manufacturing environments. This contribution addresses a critical gap in automated quality control, reducing reliance on expensive, high-precision systems while maintaining practical utility. Emmanuel’s research highlights the potential of human-robot collaboration to democratize advanced manufacturing technologies, making them viable for smaller enterprises. His work is notable for bridging the gap between theoretical robotics and real-world industrial application, with implications for streamlining production workflows and enhancing efficiency.