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Vision
Vision
KAIST Advanced & Intelligent (AI) Platform Center for Manufacturing aims to foster regional bases for the realization of the future autonomous manufacturing. The goal is to develop and integrate core autonomous manufacturing technologies, including digital materials, process AI, smart logistics, robotic manufacturing, robot-human collaboration and autonomous driving equipment, and implement them in a verifiable manufacturing testbed environment. The center ultimately wil take the central role to establish a system for the diffusion of the technology and the cultivatgion of convergent talent through industry-academia-research collaboration.
Leadership
Leadership
Prof. Jeong Whan Yoon
Prof. Jeong Whan Yoon
Director of AIPCM at KAIST
Professor in Mechanical Engineering at KAIST
Fellow, the Korea Academy of Science and Technology
Associate Editor, International Journal of Plasticity
Direct or Computer-Aided Net Shape Manufacturing Lab (website)
Email: j.yoon at kaist.ac.kr
Core Faculty
Core Faculty
Prof. Young Jae Jang
Prof. Young Jae Jang
Professor in Industrial and Systems Engineering at KAIST
Director of DAIM Labs (website)
Prof. Huitaek Yun
Prof. Huitaek Yun
Assistant Professor in Mechanical Engineering at KAIST
Director of Smart Manufacturing Systems Laboratory (website)
Prof. Minkyun Noh
Prof. Minkyun Noh
Assistant Professsor in Mechancial Engineering at KAIST
Director of Precision Mechatronics Laboratory (website)
Prof. Seungchul Lee
Prof. Seungchul Lee
Associate Professor in Mechanical Engineering at KAIST
Director of Industrial AI Lab (website)
Prof. Ki-Uk Kyung
Prof. Ki-Uk Kyung
Associate Professor in Mechancial Engineering at KAIST
Director of Human Robot Interaction (HRI) Lab (website)
Prof. Hyun-Jung Kim
Prof. Hyun-Jung Kim
Associate Professor in Industrial & Systems Engineering at KAIST
Director of Manufacturing Services Systems Lab (website)
Prof. Sanha Kim
Prof. Sanha Kim
Associate Professor in Mechanical Engineering at KAIST
Director of Advanced Manufacturing & Surface Engineering Lab (website)
Research
Research
Smart Material Characterization & Process AI Platform
Smart Material Characterization & Process AI Platform
Dynamic Material Testing for Advanced Alloys
Ai & Date-driven process optimization
Multiphysics-informed Neural Network for Heat Transfer
Integrated Scheduling via Digital Twin
Integrated Scheduling via Digital Twin
Digital Twin-based Robot Orchestration System and Deadlock Prevention Algorithm
Worker-Acceptable AI Scheduler, GNN-based Dispatching Algorithm, DRL-based Robotic Cell Scheduler
Robot Control via Cyber-Physical System
Robot Control via Cyber-Physical System
Immersive and Interactive cyber-physical system
Collaborative robot manufacturing system
Skin-attached haptic patch with force sensor incorporated into soft robotic gripper
Autonomous Manufacturing Equipments & Humanoids
Autonomous Manufacturing Equipments & Humanoids
Scaled testbed for validating urban autonomous driving
Autonomous process optimization using deep learning
Teaching
Teaching
Smart Factory for Human-Machine Collaboration
인간-기계 협업을 위한 스마트 팩토리 Smart Factory for Human-Machine Collaboration
We provide an interdisciplinary understanding of the autonomous production and deliberate the technological advancement orientations in smart factories to address with various challenges.
Understanding of Materials and Processing for Advanced Manufacturing Industries
첨단제조산업을 위한 재료와 가공의 이해Understanding of Materials and Processing for Advanced Manufacturing Industries
We teach and discuss the basic principles of engineering materials and processes in the field of advanced manufacturing industries, including small-scale (semiconductors, MEMs), large-yet-thin-scale (batteries, displays), large-scale (robots, vehicles), ultralarge-scale (aircrafts, ships).
Core Testbed Facilities (Opened for Collaborations)
Core Testbed Facilities (Opened for Collaborations)
- Smart Table-Top 5-axis CNC Machine
A smart CNC system that autonomously adjusts process parameters to improve surface quality and productivity.
Composed of a deep learning-based monitoring apparatus capable of on-site surface roughness prediction with a 3.6 % mean error and a dataset of optimized parameters generated via multi-objective Bayesian optimization in only eleven attempts, it successfully conducted a fully autonomous trochoidal slotting operation, improving the final roughness by 36 %. Related Article
Contact Us
Contact Us
291 Daehak-ro, KAIST, Bldg N7-3, 1st floor.
Yuseong-gu, Daejeon 34141, South Korea
TEL: +82-42-350-8291
Email: simisbro at kaist.ac.kr
This Center is supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(RS-2025-25406725 )
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