Papers
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Total Citations
2
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About
Deyu Tang is a researcher specializing in intelligent manufacturing and robotic welding process control, with a particular focus on real-time quality monitoring and adaptive control systems. His most cited work, "Research on Quality Control of Arc Welding Robot Based on Molten Pool Contour Extraction" (2019), addresses a critical challenge in automated welding: maintaining consistent weld quality despite process uncertainties. Tang proposed a multi-information fusion detection method that integrates adaptive Wiener filtering to extract geometric features from molten pool images, enabling precise contour analysis. This approach significantly enhances the robustness of robotic welding systems by compensating for environmental and material variability. While his citation count is currently modest, his contributions are foundational to the development of vision-based, closed-loop control strategies for industrial robots. Tang’s work bridges computer vision, signal processing, and manufacturing engineering, offering practical solutions for improving automation reliability. His research is particularly relevant for students and engineers working on smart factory technologies, where real-time process monitoring is essential for defect reduction and production efficiency.
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Top Papers
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