Wenjing Ren
Papers
3
Total Citations
144
H-Index
3
About
Wenjing Ren is a researcher specializing in intelligent manufacturing, robotic arc welding, and real-time process monitoring. His work sits at the intersection of machine learning, optical sensing, and welding quality control, with a particular focus on developing automated defect detection systems for aluminum alloy welding applications. Ren's most impactful contribution is his development of random forest-based real-time defect detection methods using optical spectrum analysis in robotic arc welding, a 2019 study that has garnered 94 citations and established him as a key voice in intelligent welding systems. Building on this foundation, his 2020 work on integrating learning approaches with optical spectroscopy for seam defect identification further advanced the field, accumulating 47 citations and demonstrating the practical scalability of his methods. Beyond optical sensing, Ren has also explored audible sound as a diagnostic tool for monitoring welding penetration, investigating frequency selection techniques to identify weld quality states in real time — reflecting his broader commitment to multi-modal sensing strategies for intelligent robotic manufacturing. Together, his research contributes meaningfully to the automation of quality assurance in industrial welding, with clear implications for manufacturing reliability and efficiency.
Research Focus
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Top Papers
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