Sanghun Shin
Papers
2
Total Citations
59
H-Index
2
About
Sanghun Shin is a researcher advancing the frontiers of smart materials and thermal engineering. His work centers on two key areas: the additive manufacturing of stimuli-responsive composites and the application of deep learning to thermal diagnostics. In a pioneering 2020 study, Shin demonstrated a novel method for fabricating reversible thermo-responsive composites using a PLA/paper bilayer via 3D printing, systematically investigating how raster angle influences actuation behavior. This work, cited 31 times, offers a scalable, low-cost approach to creating materials that change shape in response to temperature—with implications for soft robotics and adaptive structures. Complementing this, his 2021 paper (28 citations) introduced a noncontact thermal mapping technique that leverages deep neural network regression to reconstruct full-field temperature distributions from sparse local data. This innovation bridges experimental thermography and machine learning, enabling high-resolution thermal analysis without dense sensor arrays. Shin’s contributions are notable for their interdisciplinary creativity, merging materials science, manufacturing, and data-driven modeling. His research not only provides practical fabrication strategies but also opens new pathways for intelligent thermal management in engineering systems.
Research Focus
Key Achievements
Top Papers
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