Physics Informed Neural Network (Digital Twins) for Process Monitoring

In high-complexity manufacturing processes, where direct measurement of output quality is impractical or impossible, AI4M leverages Physics-Informed Neural Networks (PINNs) to build intelligent digital twins that estimate critical process outcomes using only indirect signals and fundamental physical laws.

The Challenge: When You Can’t Measure Quality Directly

Consider a thermal-pressure sealing process in a high-speed packaging line. The quality of a seal—critical to product integrity—depends on a precise balance of temperature, pressure, dwell time, and material behavior. Yet:
- There is no vision system that can “see” inside the seal as it forms
- Conventional sensors can't be placed at the sealing interface
- There is no real-time output directly indicating seal strength

This makes quality prediction extremely challenging using traditional AI or rule-based logic.

The Solution: PINNs as Digital Twins

Physics-Informed Neural Networks (PINNs) are a class of ML models that combine data-driven learning with the governing physical equations of the process—such as heat transfer, material deformation, and fluid dynamics.

By using known inputs like:
- Tool temperature
- Sealing pressure
- Film thickness
- Machine speed or dwell time

PINNs can estimate hidden process variables like internal heat flux, pressure distribution, or even the mechanical integrity of the seal.

The result is a digital twin—a virtual replica of the sealing process that mimics the physical behavior of the system in real-time, enabling:
- Seal quality prediction even without visual or direct feedback
- Process drift detection based on deviation from expected physics
- Fine-tuning of parameters to ensure consistent quality output

Why It Matters

- Ideal for black-box processes or where sensors can't be deployed
- Enables early detection of invisible defects
- Reduces reliance on destructive or post-process testing
- Drives true model-based control in critical operations

With AI4M’s PINN-powered digital twins, your factory gains process visibility where none existed before—bridging the gap between theoretical physics and practical AI to drive smarter manufacturing decisions.