Authors:
Alexander Courtier
1
;
Matthew Praeger
1
;
James Grant-Jacob
1
;
Christophe Codemard
2
;
Paul Harrison
2
;
Ben Mills
1
and
Michalis Zervas
1
Affiliations:
1
Optoelectronics Research Centre, University of Southampton, University Road, Southampton, SO17 1BJ, U.K.
;
2
TRUMPF Lasers UK, 6 Wellington Park, Toolbar Way, Hedge End, Southampton, SO30 2QU, U.K.
Keyword(s):
Deep Learning, Laser Cutting, Topography, Convolutional Variational Autoencoders, Neural Networks, Convolutional Neural Networks.
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.