Papers
1
Total Citations
3
H-Index
1
About
Mihir Barman is a rising researcher in advanced manufacturing and robotics, with a focus on optimizing industrial automation through artificial intelligence. His work centers on trajectory planning for robotic welding systems, where he has pioneered the integration of artificial neural networks (ANN) to circumvent complex mathematical derivations in traditional polynomial approaches. Barman’s most cited paper, "Optimizing ABB MIG welding robot through polynomial trajectory planning and artificial intelligence integration" (2025), demonstrates how AI can streamline robot motion control, reducing computational overhead while maintaining precision—a contribution that has already garnered early attention with 3 citations. This work signals his potential to reshape how industries approach robotic path optimization, bridging the gap between theoretical control methods and practical, data-driven solutions. As an emerging voice in the field, Barman’s research addresses critical challenges in manufacturing efficiency, offering scalable frameworks that could accelerate the adoption of intelligent welding systems. His interdisciplinary approach, combining mechanical engineering principles with machine learning, positions him as a promising innovator in the next generation of smart factory technologies.
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Top Papers
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