Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture
By S. Niaki, Ehsan Haghighat, Xinglong Li, Trevor Campbell, Reza Vaziri. arXiv.org, 2020
303 citations recorded by the source corpus.
We present a Physics-Informed Neural Network (PINN) to simulate the thermochemical evolution of a composite material on a tool undergoing cure in an autoclave. In particular, we solve the governing coupled system of differential equations -- including conductive heat transfer and resin cure kinetics -- by optimizing the parameters of a deep neural network using a physics-based loss function. To account for the vastly different behaviour of thermal conduction and resin cure, we design a PINN consisting of two disconnected subnetworks, and develop a sequential training algorithm that mitigates instability present in traditional training methods. Further, we incorporate explicit discontinuities into the DNN at the composite-tool interface and enforce known physical behaviour directly in the loss function to improve the solution near the interface. Finally, we train the PINN with a technique that automatically adapts the weights on the loss terms corresponding to PDE, boundary, interface, and initial conditions. The performance of the proposed PINN is demonstrated in multiple scenarios with different material thicknesses and thermal boundary conditions.
Subfields: Mechanical Engineering, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/ddcf4e2bdfad0fb524fd9c6e460aa6003de27447
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Mechanical Engineering
In Statistical and Nonlinear Physics
- Gianluigi Rozza
- Fabio Giampaolo
- Salvatore Cuomo
- Francesco Piccialli
- Vincenzo Schiano Di Cola
- Yew-Soon Ong
- C. Ooi
- Pao-Hsiung Chiu
Citations in the corpus
- CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method (2021, background): “PINNs have been demonstrated for various physics phenomena, including heat transfer [22,23], fluid dynamics [24–26] and electromagnetic propagation [27–29].”
- Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next (2022, background): “For a composite material, Amini Niaki et al (2021) study a system of equations, that models heat transfer with the known heat equation, ∂T ∂t = a ∂2T ∂x2 + b dα dt where a, b are parameters, and a second equation for internal heat generation expressed as a derivative of time of the degree of cure α…”