Papers
15
Total Citations
397
H-Index
9
About
Lequn Chen is an accomplished researcher at the forefront of additive manufacturing, smart manufacturing, and robotics, with a particular focus on laser-directed energy deposition (L-DED) processes. His most significant contributions center on developing multisensor fusion-based digital twin frameworks that enable real-time, in-situ quality monitoring and defect correction in robotic additive manufacturing systems — work that has garnered over 124 citations and established him as a leading voice in the field. Chen's research elegantly bridges advanced sensing technologies — including infrared thermal imaging, laser line scanning, and acoustic sensors — with machine learning algorithms to achieve localized defect detection and adaptive process control. His innovative multimodal sensor fusion approaches address longstanding limitations of single-sensor monitoring systems, significantly improving reliability in complex manufacturing environments. Beyond additive manufacturing, he has contributed meaningfully to smart factory technologies for the automotive industry, exploring cellular manufacturing systems suited to the electric vehicle era. With a body of work spanning redundant robotic kinematics, point cloud-based surface monitoring, and intelligent process correction strategies, Chen's research collectively advances the vision of fully autonomous, self-correcting manufacturing systems. His consistently high citation counts across just a few years reflect the immediate practical relevance and scholarly impact of his contributions to next-generation manufacturing.
Research Focus
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