Soodeh Ahani
Papers
2
Total Citations
19
H-Index
2
About
Soodeh Ahani is a researcher at the forefront of intelligent manufacturing, specializing in the integration of deep learning and computer vision for automated welding systems. Her work addresses a critical challenge in modern robotics: enabling pre-programmed welding robots to adapt to the uncertainties of small and medium batch production. Ahani’s major contribution lies in developing vision-based seam tracking models, particularly her keypoint detection deep learning approach for gas metal arc welding (GMAW) fillet joints, which has already garnered 17 citations since its 2024 publication. This work significantly enhances robotic adaptability in noisy welding environments, improving both efficiency and weld quality. Expanding on this foundation, her 2025 study on multi-modal data fusion for real-time defect classification represents a pioneering step toward more robust quality control in manufacturing. By combining diverse sensor inputs, Ahani’s models achieve superior accuracy in detecting weld defects during production. Her research is highly impactful for students and engineers working on Industry 4.0 applications, bridging the gap between theoretical deep learning and practical, real-world automation challenges. Ahani’s innovative approach positions her as a rising leader in smart welding technology.
Research Focus
Key Achievements
Top Papers
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