VinUniversity · Vietnam

MEDAL

Multifunctional Engineering, Dynamics, and Automation Lab

Physical AI, intelligent sensing, printed electronics, advanced manufacturing, and robotics for real engineering systems.

MEDAL develops intelligent engineering systems that connect physical processes, sensing technologies, manufacturing science, printed electronics, robotics, infrastructure monitoring, digital twins, cognitive twins, and artificial intelligence. Our research aims to improve the safety, reliability, autonomy, and performance of future engineering systems.

Explore Research Join the Lab Collaborate with Us

Sense

physical signals

Reason

with knowledge

Predict

system behavior

Act

in the real world

01 — About

About MEDAL

The Multifunctional Engineering, Dynamics, and Automation Lab — MEDAL develops technologies that help machines, sensors, manufacturing systems, printed electronic devices, robotic platforms, and critical infrastructure understand and respond to the physical world.

Led by Professor Simon S. Park at VinUniversity, MEDAL combines experimental engineering, intelligent sensing, knowledge representation, AI-based reasoning, printed electronics, advanced manufacturing, robotics, digital twins, cognitive twins, and human-computer interaction.

We are especially interested in research that bridges the gap between physical systems and intelligent decision-making.

02 — Research areas

Multidisciplinary programs. One coherent vision.

MEDAL's work spans a growing set of interlocking, multi-disciplinary research programs, unified by a Physical-AI perspective on sensing, reasoning, and action across engineering systems.

Physical AI for Manufacturing and Automation

AI-enabled frameworks that allow machines, manufacturing systems, and cyber-physical platforms to understand, reason, and adapt to physical environments.

Ontology and knowledge graph development for manufacturing and automation
Retrieval-augmented generation for engineering decision support
Digital twins and cognitive twins for monitoring, prediction, and control
AI-assisted diagnostics and process understanding
Knowledge-based reasoning for smart factories and autonomous systems
Integration of domain knowledge, sensor data, physics, and contextual reasoning

Novel Sensors and Intelligent Sensing Systems

Sensing technologies for safety-critical and high-performance engineering applications.

Gas sensors for early fault detection in energy storage systems
Sensors for abnormal chemical, thermal, and environmental signatures
Force, strain, and stress sensors for mechanical and robotic systems
Sensor integration, signal processing, and intelligent interpretation
Sensing systems for fault diagnosis, safety monitoring, and control
Data-driven and physics-informed sensor analytics

Oil and Gas Monitoring

Intelligent monitoring for oil and gas infrastructure, focused on pipeline health, leakage detection, and early warning.

Pipeline health monitoring
Leakage detection and localization
Sensor-based monitoring of oil and gas infrastructure
Fault diagnosis and early warning systems
Data-driven and physics-informed models for pipeline integrity
Integration of sensing, digital twins, and AI reasoning for infrastructure
Predictive maintenance for critical energy infrastructure

Advanced Manufacturing and Process Intelligence

Manufacturing process monitoring, diagnostics, precision, reliability, and intelligent automation.

Machining dynamics and process monitoring
Chatter detection, prediction, and suppression
Tool condition monitoring and fault diagnostics
Sensor-based manufacturing intelligence
Data-driven and physics-informed models for manufacturing processes
Intelligent process monitoring for machining and automation

3D Printing of Nanocomposites

Additive manufacturing of functional nanocomposites for sensing, enhanced material performance, and multifunctional components.

3D printing of polymer nanocomposites
Functional materials for sensing applications
Nanocomposites with enhanced mechanical, electrical, and multifunctional properties
Process–property–performance relationships in printed materials
Smart materials for structural and sensing applications
Additive manufacturing of sensor-integrated components

Printed Electronics and IPL Processing

Printed electronics and rapid photonic processing using Intense Pulsed Light for functional devices, sensors, and flexible electronic systems.

Printed electronics for sensors, flexible devices, and functional components
Intense Pulsed Light sintering and photonic curing of conductive materials
Processing of metallic, nanomaterial-based, and composite inks
Low-temperature, rapid processing for flexible or temperature-sensitive substrates
Printed conductive traces, electrodes, and sensing structures
Integration with sensors, robotics, energy systems, and smart manufacturing
Scalable manufacturing of smart and functional electronic surfaces

Robotic Grippers and Human-Computer Interaction

Robotic and human-machine systems that improve interaction between humans, machines, and intelligent devices.

Feedback-sensing robotic grippers
Tactile and force sensing for robotic manipulation
Human-computer interaction using EEG signals
Interfaces for assistive, robotic, and intelligent manufacturing applications
Human-machine collaboration and intelligent control
Sensor-enabled robotic manipulation and adaptive grasping

Ontology and Cognitive Twins for BESS

Knowledge-enhanced cognitive digital twins for immersion-cooled battery energy storage systems, connecting thermal-fluid behavior to evidence-grounded, safety-aware operating decisions.

Ontology and knowledge graph schemas for immersion-cooled BESS assets, coolant, sensors, and procedures
Cognitive digital twins linking thermal-fluid models to semantic reasoning
Fault explanation and diagnosis grounded in documented evidence
Safety-aware, human-in-the-loop operating decision support
Taxonomy and evaluation frameworks for trustworthy cognitive twins

03 — Our approach

Sense → Understand → Reason → Predict → Act

01
Sense
We design and integrate sensors to capture physical, chemical, mechanical, electrical, and biological signals from real engineering systems.
02
Understand
We process sensor data and extract meaningful patterns from complex systems, materials, machines, infrastructure, and human-machine interactions.
03
Reason
We use ontologies, knowledge graphs, RAG-based reasoning, AI models, and engineering knowledge to support intelligent decision-making.
04
Predict
We build digital twins, cognitive twins, physics-informed models, and data-driven models to forecast behavior and detect abnormal conditions.
05
Act
We apply intelligent control, robotics, automation, manufacturing intelligence, and human-machine interfaces to improve real-world performance.

MEDAL connects physical experiments, intelligent sensing, computational reasoning, and deployable engineering solutions.

04 — Applications

Where our work is applied

Our research targets concrete engineering problems in industry, infrastructure, and next-generation intelligent systems.

01Smart manufacturing

02Industrial automation

03Energy storage safety

04Oil and gas pipeline monitoring

05Infrastructure health monitoring

06Leakage detection and fault diagnosis

07Robotic manipulation

08Human-machine interaction

09Predictive maintenance

10Intelligent sensing

11Printed electronics

12Flexible sensors

13Functional conductive patterns

14Smart surfaces

15Sensor-integrated components

16Flexible and wearable electronics

17Advanced materials

18Functional 3D-printed components

19Digital and cognitive twin systems

20AI-assisted engineering decision support

05 — Featured projects

Featured Projects

A selection of active research directions. Each project links physical experimentation with sensing, modeling, or AI-based reasoning.

P.01

Physical AI · Knowledge

Knowledge Graphs and RAG for Manufacturing Reasoning

Ontology-based and retrieval-augmented reasoning systems for manufacturing diagnostics, process understanding, decision support, and automation.

P.02

Sensing · Safety

Early Fault Detection Sensors for Energy Storage

Gas-sensing technologies for early identification of abnormal conditions and safety risks in energy storage systems.

P.03

Infrastructure · Digital twin

Pipeline Health and Leakage Monitoring

Sensors, signal processing, digital twins, and AI reasoning integrated for pipeline integrity monitoring and leakage detection.

P.04

Manufacturing · Diagnostics

Chatter Detection and Fault Diagnostics in Machining

Monitoring and diagnostic methods for machining stability, tool condition, process reliability, and intelligent manufacturing.

P.05

Materials · Additive

3D-Printed Nanocomposite Sensors

Functional nanocomposites and additive manufacturing for smart sensing, enhanced properties, and sensor-integrated components.

P.06

Printed electronics · IPL

Printed Electronics using Intense Pulsed Light

Printed conductive patterns, electrodes, flexible sensors, and functional electronic structures via IPL photonic sintering.

P.07

Robotics · Sensing

Feedback-Sensing Robotic Gripper

Robotic grippers with embedded sensing for force feedback, tactile interaction, adaptive manipulation, and intelligent control.

P.08

HCI · Neural signals

EEG-Based Human-Computer Interaction

Human-computer interaction using EEG signals for assistive systems, robotics, intelligent interfaces, and human-machine collaboration.

P.09

Energy Storage · Cognitive Twin

Digital and Cognitive Twins for Immersion-Cooled BESS

A cognitive digital twin for immersion-cooled BESS, using an ontology-based knowledge graph to explain faults and support safe operating decisions.

06 — People

People

MEDAL brings together students and researchers from mechanical engineering, manufacturing, robotics, sensing, artificial intelligence, materials, printed electronics, and intelligent systems.

Principal Investigator

Professor Simon S. Park

Multifunctional Engineering, Dynamics, and Automation Lab
VinUniversity — Hanoi, Vietnam

Ph.D. in Mechanical Engineering from the University of British Columbia; was Professor of Mechanical and Manufacturing Engineering at the University of Calgary (2004–2025). Elected Fellow of the International Academy of Production Engineers (CIRP) in 2024. Has led international research programs in advanced manufacturing, digital twins, sensors, and energy systems with over $37M in funding, and co-founded multiple startups in energy, AI, healthcare, and clean technologies.

simon.p@vinuni.edu.vn

Office I431

Postdoctoral Researchers

Hau Huu HoGas sensors

The Xuan Tran

Research Assistants

Duong Luong Hong3D nanocomposite printing

Dang Hai DangPhysical AI

Anh Tuan HoangMachining cognitive twins

Quang Thieu DinhCognitive & digital twins

Tu Anh Nguyen

Undergraduate UROP Students

Anh Nguyet Dang

Anh Quoc Do

Jamie Nguyen

Tam Minh Phuc Mai

Phu Gia Ngo

Minh Anh Dang Nguyen

Duc Trung Nguyen

Nam The Nguyen

Duy Nhat Dinh Tran

Truong The Tran

Cuong Viet Tran

07 — Publications

Publications

Professor Simon S. Park's full publication record, spanning micro-machining, printed and composite electronics, and manufacturing process modeling.

View publications on Google Scholar

08 — Join

Join MEDAL

MEDAL welcomes motivated students interested in mechanical engineering, manufacturing, robotics, sensors, artificial intelligence, materials, printed electronics, and intelligent systems. Students who enjoy both hands-on experimentation and computational research are especially encouraged to apply.

Strong-fit backgrounds

Mechanical engineeringManufacturing and machiningRobotics and automationSensors and instrumentationAI and machine learningKnowledge graphs, ontologies, reasoningSignal processingMaterials and nanocompositesPrinted electronicsFlexible electronicsDigital twins and cyber-physical systemsHuman-computer interactionEnergy systems and infrastructure

We are looking for students who want to build, test, model, reason, and solve real engineering problems.

Prospective Students

09 — Collaborate

Collaborate with MEDAL

MEDAL is open to academic and industrial collaboration across Physical AI, smart manufacturing, sensing systems, printed electronics, energy storage safety, oil and gas monitoring, robotics, advanced materials, digital twins, and intelligent automation. We are especially interested in projects that connect real engineering problems with deployable solutions.

Industry-sponsored research

Joint academic projects

Student research training

Prototype development

Sensor and monitoring solutions

Manufacturing diagnostics

AI and digital twin systems

Printed electronics and functional devices

Oil and gas infrastructure monitoring

Robotic and human-machine systems

Have a real engineering system that needs to sense, reason, predict, or act? Let's collaborate.

Start a Collaboration

10 — Contact

Contact MEDAL

MEDAL — Multifunctional Engineering, Dynamics, and Automation Lab

VinUniversity
Hanoi, Vietnam

Principal Investigator

Professor Simon S. Park

simon.p@vinuni.edu.vn

Office I431