Introduction to Hybrid Modelling for Digital Twins
Physics-informed Machine Learning (PIML) involves embedding established domain knowledge (i.e. physics, chemistry, biology) with machine learning (ML) to effectively model dynamic industrial systems. While these dynamic systems face challenges such as high sensor noise and sparse measurements, they often are characterized by some fundamental scientific/engineering knowledge. There are 3 general ways to embed domain knowledge with ML, including:
- Introducing observational bias to the data
- Introducing inductive bias into the model structure
- Introducing learning bias to how models are trained
Physics-informed neural networks (PINNs) are a novel approach that integrate the information from both process data and engineering knowledge by embedding the ODEs into the loss function of a neural network. PIML integrates data and mathematical models seamlessly even in noisy and high- dimensional contexts.Thanks to its natural capability of blending physical models and data as well as the use of automatic differentiation, PIML is well placed to become an enabling catalyst in the emerging era of digital twins.
With physics-informed AI, machine operators can trust and verify
The first PINN applications are emerging in manufacturing processes with complex models and relations, such as in additive manufacturing, Van der Auweraer said.
Other early adopters will be in the food industry or pharmaceutical processing industry where complex processes may hinder a pure simulation-based approach and where the AI in a PINN approach may yield promising results, Van der Auweraer and Mas said.
PINN models also can complement or replace labor-intensive lab testing and design, Mas said, combining the existing strengths of lab testing and the benefits of physics-based simulations to accurately design new material and products in much less time using less lab testing.
Physics-Informed Neural Networks (PINNs) for Improving a Thermal Model in Stereolithography Applications
Stereolithography (SLA), additive manufacturing (3D printing) technique, is widely used nowadays for rapid prototyping and manufacturing (RP & M). This technique is driven by photo-polymerisation, which is an exothermal process. This may lead to thermal stresses significantly affecting the final quality of printed parts/products. To guarantee high-quality parts printed with the SLA technique, understanding the thermal behaviour is therefore crucial for optimizing the process. In this paper, the recent physics-informed neural network (PINN) methodology was employed to improve a physics-based model for predicting the thermal behaviour of SLA processes. The accuracy of the improved thermal model is demonstrated in this paper by comparing the predicted 2D temperature field with the 2D temperature field measured by a high-speed infrared thermal camera on parts printed on a production machine.