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Modelling and Optimisation of Femtosecond Laser Machining via Deep Learning (Invited)

Modelling and Optimisation of Femtosecond Laser Machining via Deep Learning (Invited)
Modelling and Optimisation of Femtosecond Laser Machining via Deep Learning (Invited)
Femtosecond laser machining is a highly precise fabrication method. However, it is extremely nonlinear and hence very challenging to model. Deep learning offers the capability for simulation of femtosecond laser machining directly from experimental data.
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Zervas, Michael
1840a474-dd50-4a55-ab74-6f086aa3f701
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Zervas, Michael
1840a474-dd50-4a55-ab74-6f086aa3f701

Mills, Benjamin, Grant-Jacob, James and Zervas, Michael (2023) Modelling and Optimisation of Femtosecond Laser Machining via Deep Learning (Invited). 2023 Conference on Lasers and Electro-Optics: Applications and Technology, San Jose McEnergy Convention Center, San Jose, United States. 07 - 12 May 2023. (In Press)

Record type: Conference or Workshop Item (Other)

Abstract

Femtosecond laser machining is a highly precise fabrication method. However, it is extremely nonlinear and hence very challenging to model. Deep learning offers the capability for simulation of femtosecond laser machining directly from experimental data.

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More information

Accepted/In Press date: 22 January 2023
Venue - Dates: 2023 Conference on Lasers and Electro-Optics: Applications and Technology, San Jose McEnergy Convention Center, San Jose, United States, 2023-05-07 - 2023-05-12

Identifiers

Local EPrints ID: 474615
URI: http://eprints.soton.ac.uk/id/eprint/474615
PURE UUID: be7a287e-78a5-42da-8fb6-f6eafb7d4694
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Michael Zervas: ORCID iD orcid.org/0000-0002-0651-4059

Catalogue record

Date deposited: 28 Feb 2023 17:33
Last modified: 16 Apr 2024 01:43

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