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Predictive visualisation of high repetition rate femtosecond machining of silica using deep learning

Predictive visualisation of high repetition rate femtosecond machining of silica using deep learning
Predictive visualisation of high repetition rate femtosecond machining of silica using deep learning
Whilst femtosecond laser machining can enable extremely high-resolution fabrication, it is a highly nonlinear process that is challenging to model when starting from basic principles and a theoretical understanding. Deep learning offers the potential for modelling complex systems directly from experimental data, and hence is a complementary alternative to traditional modelling approaches. In this work, deep learning is applied to the predictive visualisation of femtosecond laser machining of lines in a silica substrate, in a specific experimental regime where nanofoam is fabricated. The neural networks used for this task are shown to consider both the laser power and the amount of debris on the sample before machining, when predicting the appearance of the line after machining. This predictive capability provides clear evidence of the potential for deep learning to become an important tool in the understanding and optimisation of laser machining, and indeed, other complex physical phenomena.
2159-3930
3641-3652
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Zervas, Michalis N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Mills, Ben
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James A.
c5d144d8-3c43-4195-8e80-edd96bfda91b
Zervas, Michalis N.
1840a474-dd50-4a55-ab74-6f086aa3f701

Mills, Ben, Grant-Jacob, James A. and Zervas, Michalis N. (2023) Predictive visualisation of high repetition rate femtosecond machining of silica using deep learning. Optical Materials Express, 13 (12), 3641-3652. (doi:10.1364/OME.505746).

Record type: Article

Abstract

Whilst femtosecond laser machining can enable extremely high-resolution fabrication, it is a highly nonlinear process that is challenging to model when starting from basic principles and a theoretical understanding. Deep learning offers the potential for modelling complex systems directly from experimental data, and hence is a complementary alternative to traditional modelling approaches. In this work, deep learning is applied to the predictive visualisation of femtosecond laser machining of lines in a silica substrate, in a specific experimental regime where nanofoam is fabricated. The neural networks used for this task are shown to consider both the laser power and the amount of debris on the sample before machining, when predicting the appearance of the line after machining. This predictive capability provides clear evidence of the potential for deep learning to become an important tool in the understanding and optimisation of laser machining, and indeed, other complex physical phenomena.

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Accepted/In Press date: 11 November 2023
e-pub ahead of print date: 29 November 2023
Published date: 1 December 2023
Additional Information: Funding Information: Engineering and Physical Sciences Research Council (EP/P027644/1, EP/T026197/1, EP/W028786/1). Publisher Copyright: Journal © 2023.

Identifiers

Local EPrints ID: 486224
URI: http://eprints.soton.ac.uk/id/eprint/486224
ISSN: 2159-3930
PURE UUID: b9768edf-d0bc-4a4e-a27c-ddb801dc43ba
ORCID for Ben Mills: ORCID iD orcid.org/0000-0002-1784-1012
ORCID for James A. Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Michalis N. Zervas: ORCID iD orcid.org/0000-0002-0651-4059

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Date deposited: 15 Jan 2024 17:33
Last modified: 18 Mar 2024 03:16

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Contributors

Author: Ben Mills ORCID iD
Author: James A. Grant-Jacob ORCID iD
Author: Michalis N. Zervas ORCID iD

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