Integral modelling and Reinforcement Learning control of 3D liquid metal coating on a moving substrate

Fuente: arXiv
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Hauptverfasser: Pino, Fabio, Fracchia, Edoardo, Scheid, Benoit, Mendez, Miguel A.
Format: Preprint
Veröffentlicht: 2025
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author Pino, Fabio
Fracchia, Edoardo
Scheid, Benoit
Mendez, Miguel A.
author_facet Pino, Fabio
Fracchia, Edoardo
Scheid, Benoit
Mendez, Miguel A.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2503_14270
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Integral modelling and Reinforcement Learning control of 3D liquid metal coating on a moving substrate
Pino, Fabio
Fracchia, Edoardo
Scheid, Benoit
Mendez, Miguel A.
Fluid Dynamics
Mathematical Physics
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.
title Integral modelling and Reinforcement Learning control of 3D liquid metal coating on a moving substrate
topic Fluid Dynamics
Mathematical Physics
url https://arxiv.org/abs/2503.14270