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Modelling of fibre laser cutting via deep learning

Modelling of fibre laser cutting via deep learning
Modelling of fibre laser cutting via deep learning
Laser cutting is a materials processing technique used throughout academia and industry. However, defects such as striations can be formed while cutting, which can negatively affect the final quality of the cut. As the light-matter interactions that occur during laser machining are highly non-linear and difficult to model mathematically, there is interest in developing novel simulation methods for studying these interactions. Deep learning enables a data-driven approach to the modelling of complex systems. Here, we show that deep learning can be used to determine the scanning speed used for laser cutting, directly from microscope images of the cut surface. Furthermore, we demonstrate that a trained neural network can generate realistic predictions of the visual appearance of the laser cut surface, and hence can be used as a predictive visualisation tool.
1094-4087
36487-36502
Courtier, Alexander
0a50732a-ef3f-4042-82f4-9b573c8c9ee8
McDonnell, Michael, David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Codemard, Christophe A.
0a7db5d9-507e-41e3-88bb-2606402f558b
Harrison, Paul
2d67bb8e-4452-4b8f-adc0-5e6d324ac825
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michalis N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Courtier, Alexander
0a50732a-ef3f-4042-82f4-9b573c8c9ee8
McDonnell, Michael, David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Codemard, Christophe A.
0a7db5d9-507e-41e3-88bb-2606402f558b
Harrison, Paul
2d67bb8e-4452-4b8f-adc0-5e6d324ac825
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Zervas, Michalis N.
1840a474-dd50-4a55-ab74-6f086aa3f701

Courtier, Alexander, McDonnell, Michael, David Tom, Praeger, Matthew, Grant-Jacob, James, Codemard, Christophe A., Harrison, Paul, Mills, Benjamin and Zervas, Michalis N. (2021) Modelling of fibre laser cutting via deep learning. Optics Express, 29 (22), 36487-36502. (doi:10.1364/OE.432741).

Record type: Article

Abstract

Laser cutting is a materials processing technique used throughout academia and industry. However, defects such as striations can be formed while cutting, which can negatively affect the final quality of the cut. As the light-matter interactions that occur during laser machining are highly non-linear and difficult to model mathematically, there is interest in developing novel simulation methods for studying these interactions. Deep learning enables a data-driven approach to the modelling of complex systems. Here, we show that deep learning can be used to determine the scanning speed used for laser cutting, directly from microscope images of the cut surface. Furthermore, we demonstrate that a trained neural network can generate realistic predictions of the visual appearance of the laser cut surface, and hence can be used as a predictive visualisation tool.

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More information

Accepted/In Press date: 30 August 2021
e-pub ahead of print date: 21 October 2021

Identifiers

Local EPrints ID: 453091
URI: http://eprints.soton.ac.uk/id/eprint/453091
ISSN: 1094-4087
PURE UUID: 103007f0-4ac2-4b3b-9a3d-d0b231311393
ORCID for Alexander Courtier: ORCID iD orcid.org/0000-0003-1943-4055
ORCID for Matthew Praeger: ORCID iD orcid.org/0000-0002-5814-6155
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012
ORCID for Michalis N. Zervas: ORCID iD orcid.org/0000-0002-0651-4059

Catalogue record

Date deposited: 07 Jan 2022 18:48
Last modified: 28 Apr 2022 02:29

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Contributors

Author: Alexander Courtier ORCID iD
Author: Matthew Praeger ORCID iD
Author: Christophe A. Codemard
Author: Paul Harrison
Author: Benjamin Mills ORCID iD

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