Studying the Topography of Laser Cut Aluminium Using Latent Space Produced by Deep Learning

Alexander Courtier, Matthew Praeger, James Grant-Jacob, Christophe Codemard, Paul Harrison, Ben Mills, Michalis Zervas

2023

Abstract

Modelling topography resulting from laser cutting is challenging due to the highly non-linear light-matter interactions that occur during cutting. We show that unsupervised deep learning offers a data-driven capability for modelling the changes in the topography of 3mm thick, laser cut, aluminium, under different cutting conditions. This was achieved by analysing the parameter space encoded by the neural network, to interpolate between output topographies for different laser cutting parameter settings. This method enabled the use of neural network parameters to determine relationships between input laser cutting parameters, such as cutting speed or focus position, and output laser cutting parameters, such as verticality or dross formation. These relationships can then be used to optimise the laser cutting process.

Paper Citation

in Harvard Style

Courtier A., Praeger M., Grant-Jacob J., Codemard C., Harrison P., Mills B. and Zervas M. (2023). Studying the Topography of Laser Cut Aluminium Using Latent Space Produced by Deep Learning. In Proceedings of the 11th International Conference on Photonics, Optics and Laser Technology - Volume 1: PHOTOPTICS, ISBN 978-989-758-632-3, pages 49-51. DOI: 10.5220/0011631400003408

in Bibtex Style

@conference{photoptics23,
author={Alexander Courtier and Matthew Praeger and James Grant-Jacob and Christophe Codemard and Paul Harrison and Ben Mills and Michalis Zervas},
title={Studying the Topography of Laser Cut Aluminium Using Latent Space Produced by Deep Learning},
booktitle={Proceedings of the 11th International Conference on Photonics, Optics and Laser Technology - Volume 1: PHOTOPTICS,},
year={2023},
pages={49-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011631400003408},
isbn={978-989-758-632-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Photonics, Optics and Laser Technology - Volume 1: PHOTOPTICS,
TI - Studying the Topography of Laser Cut Aluminium Using Latent Space Produced by Deep Learning
SN - 978-989-758-632-3
AU - Courtier A.
AU - Praeger M.
AU - Grant-Jacob J.
AU - Codemard C.
AU - Harrison P.
AU - Mills B.
AU - Zervas M.
PY - 2023
SP - 49
EP - 51
DO - 10.5220/0011631400003408