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Identification of spatial intensity profiles from femtosecond laser machined depth profiles via neural networks

Identification of spatial intensity profiles from femtosecond laser machined depth profiles via neural networks
Identification of spatial intensity profiles from femtosecond laser machined depth profiles via neural networks
Laser machining involves many complex processes, especially when using femtosecond pulses due to the high peak intensities involved. Whilst conventional modelling, such as those based on photon-electron interactions, can be used to predict the appearance of the surface after machining, this generally becomes unfeasible for micron-scale features and larger. The authors have previously demonstrated that neural networks can simulate the appearance of a sample when machined using different spatial intensity profiles. However, using a neural network to model the reverse of this process is challenging, as diffractive effects mean that any particular sample appearance could have been produced by a large number of beam shape variations. Neural networks struggle with such one-to-many mappings, and hence a different approach is needed. Here, we demonstrate that this challenge can be solved by using a neural network loss function that is a separate neural network. Here, we therefore present a neural network that can identify the spatial intensity profiles needed, for multiple laser pulses, to produce a specific depth profile in 5 µm thick electroless nickel.
1094-4087
36469-36486
McDonnell, Michael, David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
McDonnell, Michael, David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

McDonnell, Michael, David Tom, Grant-Jacob, James, Praeger, Matthew, Eason, R.W. and Mills, Benjamin (2021) Identification of spatial intensity profiles from femtosecond laser machined depth profiles via neural networks. Optics Express, 29 (22), 36469-36486. (doi:10.1364/OE.431441).

Record type: Article

Abstract

Laser machining involves many complex processes, especially when using femtosecond pulses due to the high peak intensities involved. Whilst conventional modelling, such as those based on photon-electron interactions, can be used to predict the appearance of the surface after machining, this generally becomes unfeasible for micron-scale features and larger. The authors have previously demonstrated that neural networks can simulate the appearance of a sample when machined using different spatial intensity profiles. However, using a neural network to model the reverse of this process is challenging, as diffractive effects mean that any particular sample appearance could have been produced by a large number of beam shape variations. Neural networks struggle with such one-to-many mappings, and hence a different approach is needed. Here, we demonstrate that this challenge can be solved by using a neural network loss function that is a separate neural network. Here, we therefore present a neural network that can identify the spatial intensity profiles needed, for multiple laser pulses, to produce a specific depth profile in 5 µm thick electroless nickel.

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Accepted/In Press date: 25 August 2021
Published date: 21 October 2021

Identifiers

Local EPrints ID: 452203
URI: http://eprints.soton.ac.uk/id/eprint/452203
ISSN: 1094-4087
PURE UUID: fe1ef137-64b1-47a3-80e0-a7475ca1b0cf
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Matthew Praeger: ORCID iD orcid.org/0000-0002-5814-6155
ORCID for R.W. Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 30 Nov 2021 17:30
Last modified: 28 Apr 2022 02:04

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