Nikita Cherkasov
Papers
4
Total Citations
17
H-Index
4
About
Nikita Cherkasov is an emerging researcher specializing in automated quality control, computer vision, and non-destructive testing of 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 weld surface defects with high precision. Cherkasov's most notable contributions include the development of a laser scanning-based inspection system leveraging YOLOv5 for real-time weld defect detection, as well as pioneering the use of FANUC robotic platforms equipped with the iRVision 3DL computer vision system for automated weld quality assessment. His research directly addresses the limitations of manual inspection methods, which are both time-consuming and inconsistent, by proposing scalable, data-driven alternatives aligned with Industry 4.0 principles. His work on deep learning-based defect classification further demonstrates his commitment to advancing automated manufacturing intelligence. Although still early in his research career, Cherkasov has accumulated citations across multiple publications within a short timeframe, signaling growing interest in his contributions from the industrial and academic communities. His research holds significant practical implications for improving structural integrity and reducing quality control costs in modern steel fabrication and welding industries.
Research Focus
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Top Papers
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