Papers
4
Total Citations
186
H-Index
4
About
Chaolin Tan is an emerging researcher at the forefront of intelligent additive manufacturing, with a focused expertise in digital twin technologies, multisensor fusion, and robotic laser-directed energy deposition (DED). His work addresses one of the most pressing challenges in advanced manufacturing: ensuring real-time quality assurance and defect correction during complex build processes. By integrating multiple sensor streams into sophisticated digital twin frameworks, Tan has developed systems capable of localized quality prediction and in-situ defect detection — enabling adaptive correction before build failures occur, rather than after costly post-processing inspections. His most cited work, "Multisensor Fusion-Based Digital Twin for Localized Quality Prediction in Robotic Laser-Directed Energy Deposition" (2023), has already garnered 124 citations, a remarkable achievement for a paper less than two years old, signaling strong community interest in his approach. Collectively, his publications have accumulated nearly 200 citations, underscoring the relevance and timeliness of his contributions. For students and researchers exploring smart manufacturing, process monitoring, or Industry 4.0 applications, Tan's body of work offers a compelling blueprint for bridging artificial intelligence, sensing technology, and precision fabrication in next-generation manufacturing systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4