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Modelling laser machining of nickel with spatially shaped three pulse sequences using deep learning

Modelling laser machining of nickel with spatially shaped three pulse sequences using deep learning
Modelling laser machining of nickel with spatially shaped three pulse sequences using deep learning
Femtosecond laser machining is a complex process, owing to the high peak intensities involved. Modelling approaches for the prediction of final sample quality based on photon-atom interactions are therefore challenging to extrapolate up to the microscale and beyond. The problem is compounded when multiple exposures are used to produce a final structure, where surface modifications from previous exposures must be taken into consideration. Neural network approaches allow for the automatic creation of a model that accounts for these challenging processes, without any physical knowledge of the processes being programmed by a specialist. We present such a network for the prediction of surface quality for multi-exposure femtosecond machining on a 5µm electroless nickel layer deposited on copper, where each pulse is uniquely spatially shaped using a spatial light modulator. This neural network modelling method accurately predicts the surface profile after three, sequential, overlapping exposures of dissimilar intensity patterns. It successfully reproduces such effects as the sub-diffraction limit machining feasible with multiple exposures, and the smoothing effect on edge-burr from previous exposures expected in multi-exposure laser machining.
1094-4087
14627-14637
McDonnell, Michael, David Tom
bc7b6423-bd77-424d-81e7-4e5448e926cb
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
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
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
Praeger, Matthew
84575f28-4530-4f89-9355-9c5b6acc6cac
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

McDonnell, Michael, David Tom, Grant-Jacob, James, Xie, Yunhui, Praeger, Matthew, MacKay, Benita, Scout, Eason, R.W. and Mills, Benjamin (2020) Modelling laser machining of nickel with spatially shaped three pulse sequences using deep learning. Optics Express, 28 (10), 14627-14637. (doi:10.1364/OE.381421).

Record type: Article

Abstract

Femtosecond laser machining is a complex process, owing to the high peak intensities involved. Modelling approaches for the prediction of final sample quality based on photon-atom interactions are therefore challenging to extrapolate up to the microscale and beyond. The problem is compounded when multiple exposures are used to produce a final structure, where surface modifications from previous exposures must be taken into consideration. Neural network approaches allow for the automatic creation of a model that accounts for these challenging processes, without any physical knowledge of the processes being programmed by a specialist. We present such a network for the prediction of surface quality for multi-exposure femtosecond machining on a 5µm electroless nickel layer deposited on copper, where each pulse is uniquely spatially shaped using a spatial light modulator. This neural network modelling method accurately predicts the surface profile after three, sequential, overlapping exposures of dissimilar intensity patterns. It successfully reproduces such effects as the sub-diffraction limit machining feasible with multiple exposures, and the smoothing effect on edge-burr from previous exposures expected in multi-exposure laser machining.

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More information

Accepted/In Press date: 19 January 2020
e-pub ahead of print date: 29 April 2020
Published date: 11 May 2020

Identifiers

Local EPrints ID: 440871
URI: http://eprints.soton.ac.uk/id/eprint/440871
ISSN: 1094-4087
PURE UUID: b3a6c29b-968c-4d67-93d7-a43a3c754b4c
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Benita, Scout MacKay: ORCID iD orcid.org/0000-0003-2050-8912
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: 21 May 2020 16:30
Last modified: 23 May 2020 00:44

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