Jianrong Tan

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About

Jianrong Tan is a researcher whose work spans advanced manufacturing technologies, with particular expertise in additive manufacturing and digital twin methodologies for industrial applications. His research addresses critical challenges in layered manufacturing processes, where he has developed innovative approaches to optimize production efficiency and material usage. Most notably, his 2021 work on support diminution design for layered manufacturing introduced a variable orientation tracking (VOT) method aimed at minimizing external supports and upholders during the fabrication of manifold surfaces — a contribution that has garnered 17 citations and represents a meaningful advancement in reducing material waste and streamlining additive manufacturing workflows. More recently, Tan has extended his research interests into the domain of digital twin technology, with his 2024 work presenting an equipment-level digital twin method tailored specifically for industrial robots engaged in machining operations, reflecting the growing intersection of intelligent manufacturing and robotics. Together, these contributions position Tan as a researcher actively bridging geometric design optimization with smart manufacturing systems, making his work particularly relevant for engineers and scientists working at the forefront of Industry 4.0 and next-generation fabrication technologies.