Hong-In Won
Digital twins and manufacturing AI — and how to verify AI agents on the factory floor.
Principal Researcher · Chief of Manufacturing AI Cooperation Team · Manufacturing AI Research Center, Korea Institute of Industrial Technology (KITECH), Incheon
Adjunct Professor · HYU-KITECH Joint Department, Graduate School, Hanyang University
Bio
Hong-In Won received his Ph.D. degree in mechanical design engineering from Hanyang University, Seoul, Korea, in 2017. From 2017 to 2019, he conducted postdoctoral research in virtual engineering at the Technical University of Dresden, Germany. He is currently a Principal Researcher with the Manufacturing AI Research Center, Korea Institute of Industrial Technology (KITECH), Korea, where he serves as Chief of the Manufacturing AI Cooperation Team. He is also an Adjunct Professor in the HYU-KITECH Joint Department, Graduate School, Hanyang University, Korea. His research interests include autonomous manufacturing, manufacturing artificial intelligence, digital twins, and cyber-physical systems.
In progress · 2026FloorTruth
Manufacturing Agent Verification Protocol
A protocol and testbed for verifying AI agents on the factory floor. Agent execution logs are checked against user-supplied ground truth and constraints, and task outcomes are separated from human assistance. The current form is a set of synthetic scenarios and a log auditor — not a certification of real processes, quality or safety.
In progress · 2026DAYBREAK
From the screen to the street — an embodied showcase
An AI agent rehearses in a digital twin, then moves into the world on a small robot: it builds a body, migrates onto it, and walks out. The twin is built first and the physical run follows; the points where people help are recorded, not hidden.
Dataset · 2026Real-world reference data for industrial human–robot collaboration
Captured at the KTL Robot Testing & Certification Center with LiDAR, RGB-D, motion capture, robot state and 360° imagery in sync, alongside a Unity digital twin pipeline that generates proximity and pose ground truth. Presented at Unite Seoul 2026.
OngoingAgent workflows for manufacturing AI
Twin · commissioning · physical robots · task verification
We build agent workflows for manufacturing AI: an agent plans and acts in a digital twin, is commissioned against the twin before touching hardware, drives physical robots, and has its work verified against ground truth and constraints. The pieces are shared across FloorTruth, DAYBREAK and the twin pipeline.
Latest preprint
arXivAttenuated in-context identification in time-series foundation models: diagnosis under counterfactual inputs and repair by synthetic forced-system fine-tuningHong-In Won*
arXiv:2610.08118 · arxiv.org/abs/2610.08118
All publications — 91 publications →
A Unity Digital Twin-based Synthetic Data Pipeline for Manufacturing AI
Unite Seoul 2026 · COEX · 2026-07-21 · Talk page (slides & summary)