Guangrui Wen
Papers
9
Total Citations
631
H-Index
8
About
Guangrui Wen is a prominent researcher specializing in intelligent robotic welding, non-destructive defect detection, and machine learning applications in advanced manufacturing. His work sits at the intersection of artificial intelligence and industrial automation, with a particular focus on aluminum alloy welding processes critical to aerospace, automotive, and shipbuilding industries. Wen's most significant contributions involve developing innovative real-time monitoring systems for robotic arc welding. His 2019 paper applying deep convolutional neural networks to weld image analysis has garnered 318 citations, establishing a landmark approach to automated defect detection. Complementing this, he has pioneered the use of multimodal sensing strategies — including optical spectroscopy, audible sound analysis, and vision-based systems — to identify welding defects such as porosity and seam irregularities with high accuracy in pulsed GTAW processes. A recurring theme across his research is the intelligent fusion of multisensory data combined with advanced feature selection techniques, including random forest classifiers and integrating learning frameworks. His review of on-line robotic arc welding monitoring further demonstrates his broad command of the field. With over 600 cumulative citations, Wen's research has meaningfully advanced the push toward fully automated, intelligent quality control in precision manufacturing environments.
Research Focus
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Top Papers
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