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.
21574-21584
Heath, Daniel
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Grant-Jacob, James
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Xie, Yunhui
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MacKay, Benita, Scout
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Baker, James
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Eason, Robert
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Mills, Benjamin
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Heath, Daniel
d53c269d-90d2-41e6-aa63-a03f8f014d21
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Xie, Yunhui
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MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Baker, James
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Eason, Robert
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Mills, Benjamin
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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), .
(doi:10.1364/OE.26.021574).
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
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Available under License Other.
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oe-26-17-21574
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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
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Date deposited: 13 Jul 2018 16:30
Last modified: 16 Mar 2024 06:52
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Contributors
Author:
Daniel Heath
Author:
Yunhui Xie
Author:
Benita, Scout MacKay
Author:
James Baker
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