Aleksey Ulanov
Papers
4
Total Citations
17
H-Index
4
About
Aleksey Ulanov is an emerging researcher specializing in automated quality control, computer vision, and non-destructive testing for welded structures. His work sits at the intersection of industrial robotics, machine learning, and manufacturing engineering, with a particular focus on developing intelligent systems capable of detecting and classifying surface defects in welded joints with high precision and reliability. Ulanov's most notable contributions include pioneering the integration of laser scanning technology with the YOLOv5 deep learning architecture for weld surface defect detection, alongside the deployment of computer vision systems on industrial robotic platforms such as the FANUC robot equipped with iRVision 3DL. His research directly addresses the limitations of traditional manual inspection methods, which are slow, subjective, and incompatible with the demands of Industry 4.0 manufacturing environments. By combining deep learning classification models with automated robotic inspection, he has helped lay the groundwork for scalable, cost-effective quality assurance pipelines in steel fabrication. With a growing body of work accumulating citations across multiple 2022–2023 publications, Ulanov represents an active voice in the automation of welding quality control. His research is particularly valuable for engineers and students seeking to understand how modern AI-driven vision systems can replace or augment conventional non-destructive testing methods in real-world industrial settings.
Research Focus
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Top Papers
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