Integral modelling and Reinforcement Learning control of 3D liquid metal coating on a moving substrate
Abstract
Metallic coatings are used to enhance the durability of metal surfaces by protecting them from corrosion. These protective layers are typically deposited in a fluid state via a liquid film. Controlling instabilities in the liquid film is crucial to achieving uniform, high-quality coatings. This study explores the possibility of controlling liquid films on a moving substrate using a combination of gas jets and electromagnetic actuators. To model the 3D liquid film, we extend existing integral models to incorporate the effects of electromagnetic actuators. The control strategy was developed within a reinforcement learning framework, in which the Proximal Policy Optimisation (PPO) algorithm interacts with the liquid film via pneumatic and electromagnetic actuators to optimise a reward function that accounts for instability-wave amplitude through a trial-and-error process. The PPO identified an optimal control law that reduced interface instabilities via a novel mechanism: gas jets push crests, and electromagnets raise troughs via the Lorentz force.
Keywords
Cite
@article{arxiv.2503.14270,
title = {Integral modelling and Reinforcement Learning control of 3D liquid metal coating on a moving substrate},
author = {Fabio Pino and Edoardo Fracchia and Benoit Scheid and Miguel A. Mendez},
journal= {arXiv preprint arXiv:2503.14270},
year = {2026}
}