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Machine learning for 3D simulated visualization of laser machining

Machine learning for 3D simulated visualization of laser machining
Machine learning for 3D simulated visualization of laser machining
Laser machining can depend on the combination of many complex and nonlinear physical processes. Simulations of laser machining that are built from first-principles, such as the photon-atom interaction, are therefore challenging to scale-up to experimentally useful dimensions. Here, we demonstrate a simulation approach using a neural network, which requires zero knowledge of the underling physical processes and instead uses experimental data directly to create the model of the experiment. The neural network modelling approach was shown to accurately predict the 3D surface profile of the laser machined surface after exposure to various spatial intensity profiles, and was used to discover trends inherent within the experimental data that would have otherwise been difficult to discover.
1094-4087
21574-21584
Heath, Daniel
d53c269d-90d2-41e6-aa63-a03f8f014d21
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Baker, James
00dcd0b1-991b-4194-8221-c98019bb0b1e
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Heath, Daniel
d53c269d-90d2-41e6-aa63-a03f8f014d21
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Xie, Yunhui
f2c3b0e4-8650-4e04-80e5-04505f45bdd6
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Baker, James
00dcd0b1-991b-4194-8221-c98019bb0b1e
Eason, Robert
e38684c3-d18c-41b9-a4aa-def67283b020
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

Heath, Daniel, Grant-Jacob, James, Xie, Yunhui, MacKay, Benita, Scout, Baker, James, Eason, Robert and Mills, Benjamin (2018) Machine learning for 3D simulated visualization of laser machining. Optics Express, 26 (17), 21574-21584. (doi:10.1364/OE.26.021574).

Record type: Article

Abstract

Laser machining can depend on the combination of many complex and nonlinear physical processes. Simulations of laser machining that are built from first-principles, such as the photon-atom interaction, are therefore challenging to scale-up to experimentally useful dimensions. Here, we demonstrate a simulation approach using a neural network, which requires zero knowledge of the underling physical processes and instead uses experimental data directly to create the model of the experiment. The neural network modelling approach was shown to accurately predict the 3D surface profile of the laser machined surface after exposure to various spatial intensity profiles, and was used to discover trends inherent within the experimental data that would have otherwise been difficult to discover.

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Machine learning for 3D simulated visualization of laser machining - Accepted Manuscript
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More information

Accepted/In Press date: 28 June 2018
e-pub ahead of print date: 7 August 2018

Identifiers

Local EPrints ID: 422042
URI: http://eprints.soton.ac.uk/id/eprint/422042
ISSN: 1094-4087
PURE UUID: 363e17d0-33d5-48ec-ad03-c3d73c360c53
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 Robert 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: 13 Jul 2018 16:30
Last modified: 15 Sep 2021 04:09

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Contributors

Author: Daniel Heath
Author: Yunhui Xie
Author: James Baker
Author: Robert Eason ORCID iD
Author: Benjamin Mills ORCID iD

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