Xi-Yin Chen

About

Xi-Yin Chen is a leading researcher in intelligent manufacturing and real-time welding process monitoring, with a focus on advancing keyhole tungsten inert gas (K-TIG) welding technologies. His most influential work integrates deep learning and embedded systems to predict weld penetration in real time, a critical challenge in automated fabrication. In his landmark 2023 paper, Chen developed a segmentation-LSTM model that achieves high-accuracy penetration prediction directly on embedded hardware, enabling low-latency, on-site quality control without reliance on cloud computing. This contribution, already garnering 22 citations, bridges the gap between advanced neural architectures and practical industrial deployment, offering a scalable solution for defect prevention in thick-plate welding. Chen’s research stands out for its dual emphasis on algorithmic innovation and hardware feasibility, making him a key figure in the transition toward autonomous, data-driven manufacturing. His work not only reduces rework costs and improves weld integrity but also sets a benchmark for real-time process analytics in resource-constrained environments. For students and engineers, Chen’s approach exemplifies how tailored deep learning models can transform traditional manufacturing processes into intelligent, adaptive systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Real-time K-TIG welding penetration prediction on embedded system using a segmentation-LSTM model

   22 citations · 2023

Key Collaborators

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