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About

Guojun Wen is a researcher whose work spans industrial robotics, computer vision, and intelligent manufacturing systems. His career reflects a sustained commitment to advancing automation technologies, beginning with foundational contributions to robotic kinematics simulation. His early work on offline kinematics simulation and ADAMS-based manipulator modeling for welding robots helped establish computational frameworks for industrial robot motion planning, garnering a combined 25 citations and demonstrating his grounding in mechanical systems and robotics engineering. Wen's most significant contribution to date is his 2020 deep learning-based method for semiconductor wafer surface defect inspection, which has accumulated 85 citations and stands as a landmark achievement in applied computer vision. By leveraging deep convolutional neural networks to detect subtle manufacturing defects such as stains, burrs, scratches, and holes, his approach directly addresses quality control challenges in high-precision semiconductor production — a domain with enormous industrial relevance. Most recently, Wen extended his vision-guided expertise to horizontal directional drilling rigs, developing a 2D vision sensor method for automated drill pipe delivery in coal mining environments. Collectively, his research demonstrates a coherent trajectory from robotics simulation toward intelligent, vision-driven industrial automation systems with real-world manufacturing impact.