Arun Nandagopal
Papers
1
Total Citations
6
H-Index
1
About
Arun Nandagopal is a researcher at the forefront of advanced manufacturing and quality control, specializing in the intersection of robotics, computer vision, and machine learning. His work addresses a critical bottleneck in precision industries: the transition from manual to automated visual inspection. Nandagopal’s most cited paper, “A robotic surface inspection framework and machine-learning based optimal segmentation for aerospace and precision manufacturing” (2024, 6 citations), introduces a novel framework that integrates robotic manipulation with deep learning segmentation to autonomously detect subtle surface defects—such as scratches, dents, and discolorations—on complex aerospace components. This contribution is pivotal for industries where even microscopic imperfections can compromise safety and performance. By developing an optimal segmentation algorithm tailored for high-value parts, Nandagopal’s research directly tackles the inefficiency and subjectivity of manual inspection, offering a scalable, data-driven solution. His work not only advances the field of automated quality assurance but also provides a practical pathway for manufacturers to enhance yield, reduce costs, and ensure compliance in mission-critical applications. Nandagopal’s findings are already influencing next-generation smart factory systems, positioning him as a key innovator in precision manufacturing and industrial AI.
Research Focus
Key Achievements
Top Papers
- 1