Manufacturing AI training
Digital twin
Build a digital twin that mirrors physical equipment and processes in a virtual space in real time, then validate process change scenarios with AI-driven simulation.
What is a digital twin?
A digital twin replicates physical equipment, processes, and products in a virtual space in real time so they can be simulated, analyzed, and predicted. It lets you validate process change scenarios in a virtual environment without stopping the actual plant.
Real-time physical-to-digital sync
IoT sensor data collected over OPC-UA and MQTT keeps the virtual model matched to the real equipment as it runs.
AI-accelerated simulation
A deep learning model trained on CAE results predicts the outcome instead of running the full simulation, so decisions come faster.
What-if validation
Process parameter changes are tested repeatedly in a virtual environment, with no interruption to the real line.
What you'll learn
The core material this course covers.
- The digital twin maturity model (monitoring, simulation, prediction, autonomous control)
- AI prediction of CAE results (deep learning in place of simulation)
- Synchronizing real-time IoT data with the simulation
- What-if simulation and decision support
- Connecting 3D and CAD data
- Digital twin deployments in Korea
Curriculum
What we cover
- 1
Digital twin concepts and a manufacturing roadmap
Understand how physical-to-digital synchronization works, then walk through the four maturity stages and deployments in Korea and abroad.
- 2
Combining CAE simulation with AI prediction
Compare the speed and cost of AI prediction against conventional CAE analysis, then learn the workflow for building a surrogate model.
- 3
Synchronizing IoT data with the simulation
Cover real-time data pipeline design, OPC-UA and MQTT integration, and how to choose a platform, with hands-on work throughout.
- 4
Hands-on what-if simulation
Run process parameter change scenarios in the virtual environment, compare the outcomes, and select the best conditions.
- 5
Operating a digital twin
Cover how to keep model accuracy up and detect drift, what security requires, and how to control running costs.
How teams put this to use
Fast CAE prediction with deep learning
A deep learning model trained on CAE data predicts simulation results in real time and accumulates design data automatically as it runs.
Steel plant digital twin (POSCO)
Simulating before raw material charging produced the optimal mix, removing trial and error from the actual process.
Virtual process validation at Dream Factory (LG Innotek)
New process conditions were simulated repeatedly in a virtual environment, confirming the yield gain before it was applied to the real line.
Expected outcomes
What changes once your team puts this to work on site.
- Validating new processes and products virtually, without stopping the line, cuts the cost of trial and error.
- AI-based CAE prediction shortens analysis time and cost.
- Real-time monitoring combined with prediction makes decisions proactive.
- Design know-how and simulation history accumulate as digital assets.
Digital twin — FAQ
You can build a sensor-data digital twin without CAE. CAE is mainly used for thermal, fluid, and structural simulation, while a twin focused on process monitoring and prediction can run on IoT data and Python-based models alone.
Predictive maintenance is narrowly focused on when an individual machine will fail. A digital twin is the broader concept: it replicates equipment, processes, and the whole plant virtually and supports what-if simulation and autonomous decision-making. Predictive maintenance is one of its core building blocks.
We'll design the training around your operation
Schedule, group size, and curriculum detail all get set with you.